Identifying Risk Factors and Preferred Hospitals for Hip/Knee Replacements: An Analysis of 2019-2022 Hospital Readmission Reduction Program Data
Adeline Casali and Scott Eugley
2024-07-09
Background and Question
Since 2012, the Centers for Medicare and Medicaid Services have implemented the Hospital Readmission Reduction Program (HRRP). This program tracks hospital readmission rates and incentivizes hospitals to reduce unnecessary readmissions through financial penalties. Using the 2019-2022 readmission data from the HRRP, this analysis aims to identify the preferred and non-preferred hospitals for hip and knee replacements for a health insurance company. Furthermore, it will examine the risk factors associated with higher readmission rates for these procedures.
Question:
What risk factors are associated with hospital readmission rates for hip/knee replacements?
Motivation:
Understanding these risk factors can help health insurance companies guide patients towards hospitals with better outcomes, thereby improving patient outcomes and reducing costs associated with readmissions.
Need:
The insights from this analysis can be used to improve hospital performance, enhance patient care, and reduce costs. As of 2019, the average cost of readmission after hip/knee surgery was $8,588, and avoiding that cost would be highly beneficial for health insurance companies and consumers alike (Phillips et al., 2019).
Novelty:
Previous analyses have used these same or similar datasets with Logistic Regression and Random Forest models to identify the most important risk factors as they pertain to hospital readmission rates for hip/knee replacements. We will be trying to improve on this type of analysis by improving the performance of the models using various techniques. Prior analyses have implemented Random Forest models to extract important risk factors, but no prior analyses have used Random Forest to classify hospitals as preferred or non-preferred for hip/knee replacement, based on the important risk factors.
Hypothesis:
Hospitals with better Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores will have lower readmission rates for hip/knee replacements because higher patient satisfaction often correlates with better overall care quality and patient outcomes, including reduced complications and better post-discharge support (Edwards et al., 2015).
Data and Analysis
We will be using the datasets from the Centers for Medicare and Medicaid Services (Centers for Medicare & Medicaid Services, 2024). Our target variable will be the readmission rate after hip/knee surgery, using data from 2019-2022. We will utilize predictors from the HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) dataset as well as Timely and Effective Care, containing information on average wait times and vaccination compliance, Complications and Deaths, containing information about the frequency of deaths and complications for procedures, and Payment and Spending metrics, which includes the costs associated with procedures.
Analysis Plan:
- Data Preprocessing:
o Handle missing values, outliers, and inconsistencies in the dataset.
o Exploratory data analysis will be performed.
- Model Selection:
o Random Forest: This model will be used to determine the most significant risk factors associated with hospital readmission, post hip/knee replacement surgery. The model will then be used in a binary classification task, which will categorize hospitals as either “preferred” or “non-preferred”, based on the national average for hospital readmission post hip/knee surgery.
o Elastic Net: While prior analyses have used coefficients from Logistic Regression modeling to determine significant risk factors, this analysis will leverage L1 and L2 regularization via an Elastic Net model.
o Neural Network: After using Elastic Net and Random Forest to find significant risk factors, these risk factors will be utilized in a neural network model and used to classify hospitals as either “preferred” or “non-preferred”. Classification ability of the neural network model will then be compared with the classification ability of the Random Forest model.
- Feature Importance Analysis:
o Identify which predictors significantly influence hospital readmission rate, post hip/knee replacement surgery.
o Linear regression analysis (Elastic Net) significant risk factors will be compared to tree-based analysis (Random Forest) significant risk factors for overlap.
- Model Validation:
o Validate the model using cross-validation techniques (k-fold, nested) to ensure robustness and generalizability.
o Hyperparameter tuning will be performed. Assessment of the model’s performance will be based on accuracy, AUC, and ROC metrics.
o The 2024 data will be used as the test set for this analysis.
Assessment:
Successful Analysis:
We will consider our analysis successful if we can identify clear risk factors associated with hospital readmission rates and accurately classify hospitals as preferred or non-preferred.
Hypothesis Support:
Our hypothesis will be supported if hospitals with better HCAHPS scores demonstrate statistically significantly lower readmission rates for hip/knee replacements.
Pitfalls:
A potential pitfall of our analysis plan is data quality and completeness. The dataset does contain missing values, and it will need to be preprocessed to handle these missing values, outliers, and inconsistencies. Another potential pitfall is not having adequate computing power to implement deep learning with the size of our dataset. Lastly, a pitfall that we need to keep an eye out for is overfitting. We will know we have overfitting if the train set far outperforms the test set, in terms of model accuracy.
Exploratory Data Analysis
Data loading and preprocessing
Loading the Data (AC)
# Set the directory for the data files
filepath <- "/Users/adelinecasali/Desktop/hospitals_current_data/"
# List the files in the directory that have "Hospital.csv"
files <- list.files(path = filepath, pattern = "Hospital.csv")
# Iterate through each file in the list
for(f in 1:length(files)) {
# Read the CSV, clean column names to upper camel case, and store in "dat"
dat <- clean_names(read_csv(paste0(filepath, files[f]),
show_col_types = FALSE),
case = "upper_camel")
# Remove ".Hospital.csv" part of the file names to create variable name
filename <- gsub(".Hospital\\.csv", "", files[f])
# Assign data to a variable with the above created name
assign(filename, dat)
}
# Create a df of file names without ".Hospital.csv"
files <- gsub(".Hospital\\.csv", "", files) %>% data.frame()
# Set column name of the df to "File Name"
names(files) <- "File Name"
files %>%
kable(
format = "html",
caption = "Table 1. List of hospital-level data files.") %>%
kable_styling(bootstrap_options = c("striped", full_width = F)
)| File Name |
|---|
| Complications_and_Deaths |
| FY_2024_HAC_Reduction_Program |
| FY_2024_Hospital_Readmissions_Reduction_Program |
| HCAHPS |
| Healthcare_Associated_Infections |
| Maternal_Health |
| Medicare_Hospital_Spending_Per_Patient |
| Outpatient_Imaging_Efficiency |
| Payment_and_Value_of_Care |
| Timely_and_Effective_Care |
| Unplanned_Hospital_Visits |
Exploring and Preprocessing the FY_2024_Hospital_Readmissions_Reduction_Program dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of FY_2024_Hospital_Readmissions_Reduction_Program
head(FY_2024_Hospital_Readmissions_Reduction_Program,10)## # A tibble: 10 × 12
## FacilityName FacilityId State MeasureName NumberOfDischarges Footnote
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 SOUTHEAST HEALTH ME… 010001 AL READM-30-H… N/A NA
## 2 SOUTHEAST HEALTH ME… 010001 AL READM-30-H… 616 NA
## 3 SOUTHEAST HEALTH ME… 010001 AL READM-30-A… 274 NA
## 4 SOUTHEAST HEALTH ME… 010001 AL READM-30-P… 404 NA
## 5 SOUTHEAST HEALTH ME… 010001 AL READM-30-C… 126 NA
## 6 SOUTHEAST HEALTH ME… 010001 AL READM-30-C… 117 NA
## 7 MARSHALL MEDICAL CE… 010005 AL READM-30-A… N/A 1
## 8 MARSHALL MEDICAL CE… 010005 AL READM-30-C… 137 NA
## 9 MARSHALL MEDICAL CE… 010005 AL READM-30-P… 285 NA
## 10 MARSHALL MEDICAL CE… 010005 AL READM-30-H… 129 NA
## # ℹ 6 more variables: ExcessReadmissionRatio <chr>,
## # PredictedReadmissionRate <chr>, ExpectedReadmissionRate <chr>,
## # NumberOfReadmissions <chr>, StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## Footnote
## 12077Replacing values with NA and “Too Few to Report” values with “5”
# Use the function "replace_with_na_all()" to replace aberrant values with NA
FY_2024_Hospital_Readmissions_Reduction_Program <- replace_with_na_all(FY_2024_Hospital_Readmissions_Reduction_Program, condition = ~ .x == "N/A")
# Replace "Too Few to Report" values with "5" in using gsub
FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions <- gsub("Too Few to Report", "5", FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions)
# Check first 10 rows to confirm that it worked
head(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions, 10)## [1] "5" "149" "32" "68" "11" "20" NA "14" "40" "24"# NumberOfReadmissions had to be converted to numeric before applying integers
FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions <- as.numeric(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions)
# Find all values of "5" in NumberOfReadmissions
fives <- which(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions == 5)
# Replace values of "5" with random integers from 1 - 10
FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions[fives] <- sample(1:10, length(fives), replace = TRUE)
# Check the first 20 rows to see if this was applied correctly
head(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions,20)## [1] 8 149 32 68 11 20 NA 14 40 24 1 NA 9 21 15 83 36 75 7
## [20] NAConverting columns to numeric
# Selecting the columns to convert
columns_to_convert <- c("NumberOfDischarges", "ExcessReadmissionRatio", "PredictedReadmissionRate", "ExpectedReadmissionRate", "NumberOfReadmissions")
# Use mutate_at to convert the specified columns to numeric
FY_2024_Hospital_Readmissions_Reduction_Program <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
mutate_at(vars(one_of(columns_to_convert)), as.numeric)
# Print the structure of the dataframe to check the changes
str(FY_2024_Hospital_Readmissions_Reduction_Program)## tibble [18,774 × 12] (S3: tbl_df/tbl/data.frame)
## $ FacilityName : chr [1:18774] "SOUTHEAST HEALTH MEDICAL CENTER" "SOUTHEAST HEALTH MEDICAL CENTER" "SOUTHEAST HEALTH MEDICAL CENTER" "SOUTHEAST HEALTH MEDICAL CENTER" ...
## $ FacilityId : chr [1:18774] "010001" "010001" "010001" "010001" ...
## $ State : chr [1:18774] "AL" "AL" "AL" "AL" ...
## $ MeasureName : chr [1:18774] "READM-30-HIP-KNEE-HRRP" "READM-30-HF-HRRP" "READM-30-AMI-HRRP" "READM-30-PN-HRRP" ...
## $ NumberOfDischarges : num [1:18774] NA 616 274 404 126 117 NA 137 285 129 ...
## $ Footnote : num [1:18774] NA NA NA NA NA NA 1 NA NA NA ...
## $ ExcessReadmissionRatio : num [1:18774] 0.892 1.1 0.933 0.987 0.952 ...
## $ PredictedReadmissionRate: num [1:18774] 3.53 23.13 12.9 17.05 9.81 ...
## $ ExpectedReadmissionRate : num [1:18774] 3.96 21.02 13.83 17.28 10.31 ...
## $ NumberOfReadmissions : num [1:18774] 8 149 32 68 11 20 NA 14 40 24 ...
## $ StartDate : chr [1:18774] "07/01/2019" "07/01/2019" "07/01/2019" "07/01/2019" ...
## $ EndDate : chr [1:18774] "06/30/2022" "06/30/2022" "06/30/2022" "06/30/2022" ...Removing excess text from measure names
FY_2024_Hospital_Readmissions_Reduction_Program <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
mutate(MeasureName = gsub("READM-30-", "", MeasureName)) %>%
mutate(MeasureName = gsub("-HRRP", "", MeasureName)) Creating a dictionary for medical conditions
dict <- tribble(
~Acronym, ~Definition,
"HIP-KNEE", "Total Hip/Knee Arthroplasty",
"HF", "Heart Failure",
"COPD", "Chronic Obstructive Pulmonary Disease",
"AMI", "Acute Myocardial Infarction",
"CABG", "Coronary Artery Bypass Graft",
"PN", "Pneumonia"
)
dict %>%
kable(
format = "html",
caption = "Table 2. Acronyms of medical conditions for which hospital readmissions are tracked.") %>%
kable_styling(bootstrap_options = c("hover", full_width = F)
)| Acronym | Definition |
|---|---|
| HIP-KNEE | Total Hip/Knee Arthroplasty |
| HF | Heart Failure |
| COPD | Chronic Obstructive Pulmonary Disease |
| AMI | Acute Myocardial Infarction |
| CABG | Coronary Artery Bypass Graft |
| PN | Pneumonia |
Pivoting the data wider
readmissionsClean <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
pivot_wider(
names_from = MeasureName,
values_from = c(NumberOfDischarges, ExcessReadmissionRatio, PredictedReadmissionRate, ExpectedReadmissionRate, NumberOfReadmissions),
id_cols = c(FacilityName, FacilityId, State, StartDate, EndDate)
)
# Check the new dataframe
dim(readmissionsClean)## [1] 3129 35head(readmissionsClean)## # A tibble: 6 × 35
## FacilityName FacilityId State StartDate EndDate NumberOfDischarges_H…¹
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 SOUTHEAST HEALTH ME… 010001 AL 07/01/20… 06/30/… NA
## 2 MARSHALL MEDICAL CE… 010005 AL 07/01/20… 06/30/… NA
## 3 NORTH ALABAMA MEDIC… 010006 AL 07/01/20… 06/30/… NA
## 4 MIZELL MEMORIAL HOS… 010007 AL 07/01/20… 06/30/… NA
## 5 CRENSHAW COMMUNITY … 010008 AL 07/01/20… 06/30/… NA
## 6 ST. VINCENT'S EAST 010011 AL 07/01/20… 06/30/… NA
## # ℹ abbreviated name: ¹`NumberOfDischarges_HIP-KNEE`
## # ℹ 29 more variables: NumberOfDischarges_HF <dbl>,
## # NumberOfDischarges_AMI <dbl>, NumberOfDischarges_PN <dbl>,
## # NumberOfDischarges_CABG <dbl>, NumberOfDischarges_COPD <dbl>,
## # `ExcessReadmissionRatio_HIP-KNEE` <dbl>, ExcessReadmissionRatio_HF <dbl>,
## # ExcessReadmissionRatio_AMI <dbl>, ExcessReadmissionRatio_PN <dbl>,
## # ExcessReadmissionRatio_CABG <dbl>, ExcessReadmissionRatio_COPD <dbl>, …Filtering for only hip/knee conditions
readmissionsClean <- readmissionsClean %>%
select(FacilityName, FacilityId, State, matches("HIP-KNEE$"))Exploring and Preprocessing the HCAHPS dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of HCAHPS
head(HCAHPS,10)## # A tibble: 10 × 22
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 6 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 7 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 8 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 9 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 10 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## # ℹ 15 more variables: TelephoneNumber <chr>, HcahpsMeasureId <chr>,
## # HcahpsQuestion <chr>, HcahpsAnswerDescription <chr>,
## # PatientSurveyStarRating <chr>, PatientSurveyStarRatingFootnote <dbl>,
## # HcahpsAnswerPercent <chr>, HcahpsAnswerPercentFootnote <chr>,
## # HcahpsLinearMeanValue <chr>, NumberOfCompletedSurveys <chr>,
## # NumberOfCompletedSurveysFootnote <chr>, SurveyResponseRatePercent <chr>,
## # SurveyResponseRatePercentFootnote <chr>, StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- HCAHPS %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## PatientSurveyStarRatingFootnote
## 430641Removing footnote columns and replacing NA values
# Removing all footnote columns
HCAHPS <- HCAHPS %>%
select(-ends_with("footnote"))
# Replacing all "Not Applicable" with NA
HCAHPS <- as.data.frame(sapply(HCAHPS, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
HCAHPS <- as.data.frame(sapply(HCAHPS, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Creating a dictionary for HCAHPS questions
dictHCAHPS <- tribble(
~`Measure ID`, ~`Measure Name`,
"H-CLEAN-HSP-A-P", "Patients who reported that their room and bathroom were 'Always' clean",
"H-CLEAN-HSP-SN-P", "Patients who reported that their room and bathroom were 'Sometimes' or 'Never' clean",
"H-CLEAN-HSP-U-P", "Patients who reported that their room and bathroom were 'Usually' clean",
"H-CLEAN-HSP-STAR-RATING", "Cleanliness - star rating",
"H_CLEAN_LINEAR_SCORE", "Cleanliness - linear mean score",
"H-COMP-1-A-P", "Patients who reported that their nurses 'Always' communicated well",
"H-COMP-1-SN-P", "Patients who reported that their nurses 'Sometimes' or 'Never' communicated well",
"H-COMP-1-U-P", "Patients who reported that their nurses 'Usually' communicated well",
"H-COMP-1-STAR-RATING", "Nurse communication - star rating",
"H_COMP_1_LINEAR_SCORE", "Nurse communication - linear mean score",
"H-COMP-2-A-P", "Patients who reported that their doctors 'Always' communicated well",
"H-COMP-2-SN-P", "Patients who reported that their doctors 'Sometimes' or 'Never' communicated well",
"H-COMP-2-U-P", "Patients who reported that their doctors 'Usually' communicated well",
"H-COMP-2-STAR-RATING", "Doctor communication - star rating",
"H_COMP_2_LINEAR_SCORE", "Doctor communication - linear mean score",
"H-COMP-3-A-P", "Patients who reported that they 'Always' received help as soon as they wanted",
"H-COMP-3-SN-P", "Patients who reported that they 'Sometimes' or 'Never' received help as soon as they wanted",
"H-COMP-3-U-P", "Patients who reported that they 'Usually' received help as soon as they wanted",
"H-COMP-3-STAR-RATING", "Staff responsiveness - star rating",
"H_COMP_3_LINEAR_SCORE", "Staff responsiveness - linear mean score",
"H-COMP-5-A-P", "Patients who reported that staff 'Always' explained about medicines before giving it to them",
"H-COMP-5-SN-P", "Patients who reported that staff 'Sometimes' or 'Never' explained about medicines before giving it to them",
"H-COMP-5-U-P", "Patients who reported that staff 'Usually' explained about medicines before giving it to them",
"H-COMP-5-STAR-RATING", "Communication about medicine - star rating",
"H_COMP_5_LINEAR_SCORE", "Communication about medicines - linear mean score",
"H-COMP-6-N-P", "Patients who reported that NO, they were not given information about what to do during their recovery at home",
"H-COMP-6-Y-P", "Patients who reported that YES, they were given information about what to do during their recovery at home",
"H-COMP-6-STAR-RATING", "Discharge information - star rating",
"H_COMP_6_LINEAR_SCORE", "Discharge information - linear mean score",
"H-COMP-7-A", "Patients who 'Agree' they understood their care when they left the hospital",
"H-COMP-7-D-SD", "Patients who 'Disagree' or 'Strongly Disagree' that they understood their care when they left the hospital",
"H-COMP-7-SA", "Patients who 'Strongly Agree' that they understood their care when they left the hospital",
"H-COMP-7-STAR-RATING", "Care transition - star rating",
"H_COMP_7_LINEAR_SCORE", "Care transition - linear mean score",
"H-HSP-RATING-0-6", "Patients who gave their hospital a rating of 6 or lower on a scale from 0 (lowest) to 10 (highest)",
"H-HSP-RATING-7-8", "Patients who gave their hospital a rating of 7 or 8 on a scale from 0 (lowest) to 10 (highest)",
"H-HSP-RATING-9-10", "Patients who gave their hospital a rating of 9 or 10 on a scale from 0 (lowest) to 10 (highest)",
"H-HSP-RATING-STAR-RATING", "Overall rating of hospital - star rating",
"H_HSP_RATING_LINEAR_SCORE", "Overall hospital rating - linear mean score",
"H-QUIET-HSP-A-P", "Patients who reported that the area around their room was 'Always' quiet at night",
"H-QUIET-HSP-SN-P", "Patients who reported that the area around their room was 'Sometimes' or 'Never' quiet at night",
"H-QUIET-HSP-U-P", "Patients who reported that the area around their room was 'Usually' quiet at night",
"H-QUIET-HSP-STAR-RATING", "Quietness - star rating",
"H_QUIET_LINEAR_SCORE", "Quietness - linear mean score",
"H-RECMND-DN", "Patients who reported NO, they would probably not or definitely not recommend the hospital",
"H-RECMND-DY", "Patients who reported YES, they would definitely recommend the hospital",
"H-RECMND-PY", "Patients who reported YES, they would probably recommend the hospital",
"H-RECMND-STAR-RATING", "Recommend hospital - star rating",
"H_RECMND_LINEAR_SCORE", "Recommend hospital - linear mean score",
"H-STAR-RATING", "Summary star rating"
)
dictHCAHPS %>%
kable(
format = "html",
caption = "Table 3. Measure IDs and Measure Names from HCAHPS") %>%
kable_styling(bootstrap_options = c("hover", "full_width" = F))| Measure ID | Measure Name |
|---|---|
| H-CLEAN-HSP-A-P | Patients who reported that their room and bathroom were ‘Always’ clean |
| H-CLEAN-HSP-SN-P | Patients who reported that their room and bathroom were ‘Sometimes’ or ‘Never’ clean |
| H-CLEAN-HSP-U-P | Patients who reported that their room and bathroom were ‘Usually’ clean |
| H-CLEAN-HSP-STAR-RATING | Cleanliness - star rating |
| H_CLEAN_LINEAR_SCORE | Cleanliness - linear mean score |
| H-COMP-1-A-P | Patients who reported that their nurses ‘Always’ communicated well |
| H-COMP-1-SN-P | Patients who reported that their nurses ‘Sometimes’ or ‘Never’ communicated well |
| H-COMP-1-U-P | Patients who reported that their nurses ‘Usually’ communicated well |
| H-COMP-1-STAR-RATING | Nurse communication - star rating |
| H_COMP_1_LINEAR_SCORE | Nurse communication - linear mean score |
| H-COMP-2-A-P | Patients who reported that their doctors ‘Always’ communicated well |
| H-COMP-2-SN-P | Patients who reported that their doctors ‘Sometimes’ or ‘Never’ communicated well |
| H-COMP-2-U-P | Patients who reported that their doctors ‘Usually’ communicated well |
| H-COMP-2-STAR-RATING | Doctor communication - star rating |
| H_COMP_2_LINEAR_SCORE | Doctor communication - linear mean score |
| H-COMP-3-A-P | Patients who reported that they ‘Always’ received help as soon as they wanted |
| H-COMP-3-SN-P | Patients who reported that they ‘Sometimes’ or ‘Never’ received help as soon as they wanted |
| H-COMP-3-U-P | Patients who reported that they ‘Usually’ received help as soon as they wanted |
| H-COMP-3-STAR-RATING | Staff responsiveness - star rating |
| H_COMP_3_LINEAR_SCORE | Staff responsiveness - linear mean score |
| H-COMP-5-A-P | Patients who reported that staff ‘Always’ explained about medicines before giving it to them |
| H-COMP-5-SN-P | Patients who reported that staff ‘Sometimes’ or ‘Never’ explained about medicines before giving it to them |
| H-COMP-5-U-P | Patients who reported that staff ‘Usually’ explained about medicines before giving it to them |
| H-COMP-5-STAR-RATING | Communication about medicine - star rating |
| H_COMP_5_LINEAR_SCORE | Communication about medicines - linear mean score |
| H-COMP-6-N-P | Patients who reported that NO, they were not given information about what to do during their recovery at home |
| H-COMP-6-Y-P | Patients who reported that YES, they were given information about what to do during their recovery at home |
| H-COMP-6-STAR-RATING | Discharge information - star rating |
| H_COMP_6_LINEAR_SCORE | Discharge information - linear mean score |
| H-COMP-7-A | Patients who ‘Agree’ they understood their care when they left the hospital |
| H-COMP-7-D-SD | Patients who ‘Disagree’ or ‘Strongly Disagree’ that they understood their care when they left the hospital |
| H-COMP-7-SA | Patients who ‘Strongly Agree’ that they understood their care when they left the hospital |
| H-COMP-7-STAR-RATING | Care transition - star rating |
| H_COMP_7_LINEAR_SCORE | Care transition - linear mean score |
| H-HSP-RATING-0-6 | Patients who gave their hospital a rating of 6 or lower on a scale from 0 (lowest) to 10 (highest) |
| H-HSP-RATING-7-8 | Patients who gave their hospital a rating of 7 or 8 on a scale from 0 (lowest) to 10 (highest) |
| H-HSP-RATING-9-10 | Patients who gave their hospital a rating of 9 or 10 on a scale from 0 (lowest) to 10 (highest) |
| H-HSP-RATING-STAR-RATING | Overall rating of hospital - star rating |
| H_HSP_RATING_LINEAR_SCORE | Overall hospital rating - linear mean score |
| H-QUIET-HSP-A-P | Patients who reported that the area around their room was ‘Always’ quiet at night |
| H-QUIET-HSP-SN-P | Patients who reported that the area around their room was ‘Sometimes’ or ‘Never’ quiet at night |
| H-QUIET-HSP-U-P | Patients who reported that the area around their room was ‘Usually’ quiet at night |
| H-QUIET-HSP-STAR-RATING | Quietness - star rating |
| H_QUIET_LINEAR_SCORE | Quietness - linear mean score |
| H-RECMND-DN | Patients who reported NO, they would probably not or definitely not recommend the hospital |
| H-RECMND-DY | Patients who reported YES, they would definitely recommend the hospital |
| H-RECMND-PY | Patients who reported YES, they would probably recommend the hospital |
| H-RECMND-STAR-RATING | Recommend hospital - star rating |
| H_RECMND_LINEAR_SCORE | Recommend hospital - linear mean score |
| H-STAR-RATING | Summary star rating |
Pivoting the data wider
HCAHPSClean <- HCAHPS %>%
pivot_wider(
names_from = HcahpsMeasureId,
values_from = c(PatientSurveyStarRating, HcahpsAnswerPercent, HcahpsLinearMeanValue, SurveyResponseRatePercent),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(HCAHPSClean)## [1] 4814 375head(HCAHPSClean)## # A tibble: 6 × 375
## FacilityName FacilityId State PatientSurveyStarRat…¹ PatientSurveyStarRat…²
## <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEAL… 010001 AL <NA> <NA>
## 2 MARSHALL MEDIC… 010005 AL <NA> <NA>
## 3 NORTH ALABAMA … 010006 AL <NA> <NA>
## 4 MIZELL MEMORIA… 010007 AL <NA> <NA>
## 5 CRENSHAW COMMU… 010008 AL <NA> <NA>
## 6 ST. VINCENT'S … 010011 AL <NA> <NA>
## # ℹ abbreviated names: ¹PatientSurveyStarRating_H_COMP_1_A_P,
## # ²PatientSurveyStarRating_H_COMP_1_SN_P
## # ℹ 370 more variables: PatientSurveyStarRating_H_COMP_1_U_P <chr>,
## # PatientSurveyStarRating_H_COMP_1_LINEAR_SCORE <chr>,
## # PatientSurveyStarRating_H_COMP_1_STAR_RATING <chr>,
## # PatientSurveyStarRating_H_NURSE_RESPECT_A_P <chr>,
## # PatientSurveyStarRating_H_NURSE_RESPECT_SN_P <chr>, …Exploring and Preprocessing the Timely_and_Effective_Care dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of Timely_and_Effective_Care
head(Timely_and_Effective_Care,10)## # A tibble: 10 × 16
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 6 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 7 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 8 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 9 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 10 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## # ℹ 9 more variables: TelephoneNumber <chr>, Condition <chr>, MeasureId <chr>,
## # MeasureName <chr>, Score <chr>, Sample <chr>, Footnote <chr>,
## # StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- Timely_and_Effective_Care %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## named list()Replacing NA values
# Replacing all "Not Applicable" with NA
Timely_and_Effective_Care <- as.data.frame(sapply(Timely_and_Effective_Care, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
Timely_and_Effective_Care <- as.data.frame(sapply(Timely_and_Effective_Care, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Creating a dictionary Timely and Effective Care measure names
dictCare <- tribble(
~`Measure ID`, ~`Measure Name`,
"EDV", "Emergency department volume (alternate Measure ID: EDV-1)",
"ED-2", "Average (median) admit decision time to time of departure from the emergency department for emergency department patients admitted to inpatient status",
"IMM-3", "Healthcare workers given influenza vaccination",
"HCP COVID-19", "COVID-19 Vaccination Coverage Among HCP",
"OP-18b", "Average (median) time patients spent in the emergency department before leaving from the visit (alternate Measure ID: OP-18)",
"OP-18c", "Average time patients spent in the emergency department before being sent home (Median Time from ED Arrival to ED Departure for Discharged ED Patients – Psychiatric/Mental Health Patients) *This measure is only found in the downloadable database, it is not displayed on Hospital Care Compare",
"OP-22", "Percentage of patients who left the emergency department before being seen",
"OP-23", "Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival",
"OP-29", "Percentage of patients receiving appropriate recommendation for follow-up screening colonoscopy",
"OP-31", "Percentage of patients who had cataract surgery and had improvement in visual function within 90 days following the surgery",
"SEP-1", "Severe Sepsis and Septic Shock",
"SEP-SH-3HR", "Septic Shock 3 Hour",
"SEP-SH-6HR", "Septic Shock 6 Hour",
"SEV-SEP-3HR", "Severe Sepsis 3 Hour",
"SEV-SEP-6HR", "Severe Sepsis 6 Hour",
"STK-02", "Percentage of ischemic stroke patients prescribed or continuing to take antithrombotic therapy at hospital discharge",
"STK-03", "Percentage of ischemic stroke patients with atrial fibrillation/flutter who are prescribed or continuing to take anticoagulation therapy at hospital discharge",
"STK-05", "Percentage of ischemic stroke patients administered antithrombotic therapy by the end of hospital day 2",
"STK-06", "Percentage of ischemic stroke patients who are prescribed or continuing to take statin medication at hospital discharge",
"VTE-1", "Percentage of patients that received VTE prophylaxis after hospital admission or surgery",
"VTE-2", "Percentage of patients that received VTE prophylaxis after being admitted to the intensive care unit (ICU)",
"Safe Use of Opioids", "Percentage of patients who were prescribed 2 or more opioids or an opioid and benzodiazepine concurrently at discharge"
)
dictCare %>%
kable(
format = "html",
caption = "Table 4. Measure IDs and Measure Names from Timely and Effective Care") %>%
kable_styling(bootstrap_options = c("hover", "full_width" = F))| Measure ID | Measure Name |
|---|---|
| EDV | Emergency department volume (alternate Measure ID: EDV-1) |
| ED-2 | Average (median) admit decision time to time of departure from the emergency department for emergency department patients admitted to inpatient status |
| IMM-3 | Healthcare workers given influenza vaccination |
| HCP COVID-19 | COVID-19 Vaccination Coverage Among HCP |
| OP-18b | Average (median) time patients spent in the emergency department before leaving from the visit (alternate Measure ID: OP-18) |
| OP-18c | Average time patients spent in the emergency department before being sent home (Median Time from ED Arrival to ED Departure for Discharged ED Patients – Psychiatric/Mental Health Patients) *This measure is only found in the downloadable database, it is not displayed on Hospital Care Compare |
| OP-22 | Percentage of patients who left the emergency department before being seen |
| OP-23 | Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival |
| OP-29 | Percentage of patients receiving appropriate recommendation for follow-up screening colonoscopy |
| OP-31 | Percentage of patients who had cataract surgery and had improvement in visual function within 90 days following the surgery |
| SEP-1 | Severe Sepsis and Septic Shock |
| SEP-SH-3HR | Septic Shock 3 Hour |
| SEP-SH-6HR | Septic Shock 6 Hour |
| SEV-SEP-3HR | Severe Sepsis 3 Hour |
| SEV-SEP-6HR | Severe Sepsis 6 Hour |
| STK-02 | Percentage of ischemic stroke patients prescribed or continuing to take antithrombotic therapy at hospital discharge |
| STK-03 | Percentage of ischemic stroke patients with atrial fibrillation/flutter who are prescribed or continuing to take anticoagulation therapy at hospital discharge |
| STK-05 | Percentage of ischemic stroke patients administered antithrombotic therapy by the end of hospital day 2 |
| STK-06 | Percentage of ischemic stroke patients who are prescribed or continuing to take statin medication at hospital discharge |
| VTE-1 | Percentage of patients that received VTE prophylaxis after hospital admission or surgery |
| VTE-2 | Percentage of patients that received VTE prophylaxis after being admitted to the intensive care unit (ICU) |
| Safe Use of Opioids | Percentage of patients who were prescribed 2 or more opioids or an opioid and benzodiazepine concurrently at discharge |
Pivoting the data wider
careClean <- Timely_and_Effective_Care %>%
pivot_wider(
names_from = MeasureId,
values_from = c(Score),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(careClean)## [1] 4677 26head(careClean)## # A tibble: 6 × 26
## FacilityName FacilityId State EDV ED_2_Strata_1 ED_2_Strata_2 HCP_COVID_19
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEA… 010001 AL high <NA> <NA> 80.7
## 2 MARSHALL MEDI… 010005 AL high 148 105 79.8
## 3 NORTH ALABAMA… 010006 AL high <NA> <NA> 79
## 4 MIZELL MEMORI… 010007 AL low <NA> <NA> 57.9
## 5 CRENSHAW COMM… 010008 AL low <NA> <NA> 81.2
## 6 ST. VINCENT'S… 010011 AL high <NA> <NA> 88
## # ℹ 19 more variables: IMM_3 <chr>, OP_18b <chr>, OP_18c <chr>, OP_22 <chr>,
## # OP_23 <chr>, OP_29 <chr>, OP_31 <chr>, SAFE_USE_OF_OPIOIDS <chr>,
## # SEP_1 <chr>, SEP_SH_3HR <chr>, SEP_SH_6HR <chr>, SEV_SEP_3HR <chr>,
## # SEV_SEP_6HR <chr>, STK_02 <chr>, STK_03 <chr>, STK_05 <chr>, STK_06 <chr>,
## # VTE_1 <chr>, VTE_2 <chr>Exploring and Preprocessing the Complications_and_Deaths dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of Complications_and_Deaths
head(Complications_and_Deaths,10)## # A tibble: 10 × 18
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 6 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 7 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 8 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 9 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 10 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## # ℹ 11 more variables: TelephoneNumber <chr>, MeasureId <chr>,
## # MeasureName <chr>, ComparedToNational <chr>, Denominator <chr>,
## # Score <chr>, LowerEstimate <chr>, HigherEstimate <chr>, Footnote <chr>,
## # StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- Complications_and_Deaths %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## named list()Replacing NA values
# Replacing all "Not Applicable" with NA
Complications_and_Deaths <- as.data.frame(sapply(Complications_and_Deaths, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
Complications_and_Deaths <- as.data.frame(sapply(Complications_and_Deaths, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Creating a dictionary for Complications and Deaths measure names
dictDeaths <- tribble(
~`Measure ID`, ~`Measure Name`,
"COMP-HIP-KNEE", "Rate of complications for hip/knee replacement patients",
"PSI 90", "Serious complications (this is a composite or summary measure; alternate Measure ID: PSI-90-SAFETY)",
"PSI 03", "Pressure sores (alternate Measure ID: PSI_3_Ulcer)",
"PSI 04", "Deaths among patients with serious treatable complications after surgery (alternate Measure ID: PSI-4-SURG-COMP)",
"PSI 06", "Collapsed lung due to medical treatment (alternate Measure ID: PSI-6-IAT-PTX)",
"PSI 08", "Broken hip from a fall after surgery (alternate Measure ID: PSI_8_POST_HIP)",
"PSI 09", "Postoperative hemorrhage or hematoma rate (alternate Measure ID: PSI_9_POST_HEM)",
"PSI 10", "Kidney and diabetic complications after surgery (alternate Measure ID: PSI_10_POST_KIDNEY)",
"PSI 11", "Respiratory failure after surgery (alternate Measure ID: PSI_11_POST_RESP)",
"PSI 12", "Serious blood clots after surgery (alternate Measure ID: PSI-12-POSTOP-PULMEMB-DVT)",
"PSI 13", "Blood stream infection after surgery (alternate Measure ID: PSI_13_POST_SEPSIS)",
"PSI 14", "A wound that splits open after surgery on the abdomen or pelvis (alternate Measure ID: PSI-14-POSTOP-DEHIS)",
"PSI 15", "Accidental cuts and tears from medical treatment (alternate Measure ID: PSI-15-ACC-LAC)",
"MORT-30-AMI", "Death rate for heart attack patients",
"MORT-30-CABG", "Death rate for Coronary Artery Bypass Graft (CABG) surgery patients",
"MORT-30-COPD", "Death rate for chronic obstructive pulmonary disease (COPD) patients",
"MORT-30-HF", "Death rate for heart failure patients",
"MORT-30-PN", "Death rate for pneumonia patients",
"MORT-30-STK", "Death rate for stroke patients"
)
dictDeaths %>%
kable(
format = "html",
caption = "Table 5. Measure IDs and Measure Names from Complications and Deaths") %>%
kable_styling(bootstrap_options = c("hover", "full_width" = F))| Measure ID | Measure Name |
|---|---|
| COMP-HIP-KNEE | Rate of complications for hip/knee replacement patients |
| PSI 90 | Serious complications (this is a composite or summary measure; alternate Measure ID: PSI-90-SAFETY) |
| PSI 03 | Pressure sores (alternate Measure ID: PSI_3_Ulcer) |
| PSI 04 | Deaths among patients with serious treatable complications after surgery (alternate Measure ID: PSI-4-SURG-COMP) |
| PSI 06 | Collapsed lung due to medical treatment (alternate Measure ID: PSI-6-IAT-PTX) |
| PSI 08 | Broken hip from a fall after surgery (alternate Measure ID: PSI_8_POST_HIP) |
| PSI 09 | Postoperative hemorrhage or hematoma rate (alternate Measure ID: PSI_9_POST_HEM) |
| PSI 10 | Kidney and diabetic complications after surgery (alternate Measure ID: PSI_10_POST_KIDNEY) |
| PSI 11 | Respiratory failure after surgery (alternate Measure ID: PSI_11_POST_RESP) |
| PSI 12 | Serious blood clots after surgery (alternate Measure ID: PSI-12-POSTOP-PULMEMB-DVT) |
| PSI 13 | Blood stream infection after surgery (alternate Measure ID: PSI_13_POST_SEPSIS) |
| PSI 14 | A wound that splits open after surgery on the abdomen or pelvis (alternate Measure ID: PSI-14-POSTOP-DEHIS) |
| PSI 15 | Accidental cuts and tears from medical treatment (alternate Measure ID: PSI-15-ACC-LAC) |
| MORT-30-AMI | Death rate for heart attack patients |
| MORT-30-CABG | Death rate for Coronary Artery Bypass Graft (CABG) surgery patients |
| MORT-30-COPD | Death rate for chronic obstructive pulmonary disease (COPD) patients |
| MORT-30-HF | Death rate for heart failure patients |
| MORT-30-PN | Death rate for pneumonia patients |
| MORT-30-STK | Death rate for stroke patients |
Pivoting the data wider
deathsClean <- Complications_and_Deaths %>%
pivot_wider(
names_from = MeasureId,
values_from = c(ComparedToNational, Score),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(deathsClean)## [1] 4814 41head(deathsClean)## # A tibble: 6 × 41
## FacilityName FacilityId State ComparedToNational_C…¹ ComparedToNational_M…²
## <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEAL… 010001 AL No Different Than the… No Different Than the…
## 2 MARSHALL MEDIC… 010005 AL No Different Than the… No Different Than the…
## 3 NORTH ALABAMA … 010006 AL No Different Than the… Worse Than the Nation…
## 4 MIZELL MEMORIA… 010007 AL Number of Cases Too S… Number of Cases Too S…
## 5 CRENSHAW COMMU… 010008 AL <NA> Number of Cases Too S…
## 6 ST. VINCENT'S … 010011 AL No Different Than the… No Different Than the…
## # ℹ abbreviated names: ¹ComparedToNational_COMP_HIP_KNEE,
## # ²ComparedToNational_MORT_30_AMI
## # ℹ 36 more variables: ComparedToNational_MORT_30_CABG <chr>,
## # ComparedToNational_MORT_30_COPD <chr>, ComparedToNational_MORT_30_HF <chr>,
## # ComparedToNational_MORT_30_PN <chr>, ComparedToNational_MORT_30_STK <chr>,
## # ComparedToNational_PSI_03 <chr>, ComparedToNational_PSI_04 <chr>,
## # ComparedToNational_PSI_06 <chr>, ComparedToNational_PSI_08 <chr>, …Exploring and Preprocessing the Payment_and_Value_of_Care dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of Payment_and_Value_of_Care
head(Payment_and_Value_of_Care,10)## # A tibble: 10 × 22
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 6 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 7 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 8 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 9 010006 NORTH ALABAMA MEDICAL… 1701 V… FLORENCE AL 35630 LAUDERDALE
## 10 010006 NORTH ALABAMA MEDICAL… 1701 V… FLORENCE AL 35630 LAUDERDALE
## # ℹ 15 more variables: TelephoneNumber <chr>, PaymentMeasureId <chr>,
## # PaymentMeasureName <chr>, PaymentCategory <chr>, Denominator <chr>,
## # Payment <chr>, LowerEstimate <chr>, HigherEstimate <chr>,
## # PaymentFootnote <dbl>, ValueOfCareDisplayId <chr>,
## # ValueOfCareDisplayName <chr>, ValueOfCareCategory <chr>,
## # ValueOfCareFootnote <dbl>, StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- Payment_and_Value_of_Care %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## PaymentFootnote ValueOfCareFootnote
## 9956 10044Replacing NA values
# Replacing all "Not Applicable" with NA
Payment_and_Value_of_Care <- as.data.frame(sapply(Payment_and_Value_of_Care, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
Payment_and_Value_of_Care <- as.data.frame(sapply(Payment_and_Value_of_Care, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Creating a dictionary for Payment and Value of Care measure names
dictPayment <- tribble(
~`Measure ID`, ~`Measure Name`,
"PAYM-30-AMI", "Payment for heart attack patients",
"PAYM-30-HF", "Payment for heart failure patients",
"PAYM-30-PN", "Payment for pneumonia patients",
"PAYM_90_HIP_KNEE", "Payment for hip/knee replacement patients"
)
dictPayment %>%
kable(
format = "html",
caption = "Table 6. Measure IDs and Measure Names from Payment and Value of Care") %>%
kable_styling(bootstrap_options = c("hover", "full_width" = F))| Measure ID | Measure Name |
|---|---|
| PAYM-30-AMI | Payment for heart attack patients |
| PAYM-30-HF | Payment for heart failure patients |
| PAYM-30-PN | Payment for pneumonia patients |
| PAYM_90_HIP_KNEE | Payment for hip/knee replacement patients |
Pivoting the data wider
paymentClean <- Payment_and_Value_of_Care %>%
pivot_wider(
names_from = PaymentMeasureId,
values_from = c(PaymentCategory, Payment),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(paymentClean)## [1] 4645 11head(paymentClean)## # A tibble: 6 × 11
## FacilityName FacilityId State PaymentCategory_PAYM…¹ PaymentCategory_PAYM…²
## <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEAL… 010001 AL No Different Than the… No Different Than the…
## 2 MARSHALL MEDIC… 010005 AL No Different Than the… No Different Than the…
## 3 NORTH ALABAMA … 010006 AL Greater Than the Nati… No Different Than the…
## 4 MIZELL MEMORIA… 010007 AL Number of Cases Too S… No Different Than the…
## 5 CRENSHAW COMMU… 010008 AL Number of Cases Too S… Number of Cases Too S…
## 6 ST. VINCENT'S … 010011 AL No Different Than the… No Different Than the…
## # ℹ abbreviated names: ¹PaymentCategory_PAYM_30_AMI,
## # ²PaymentCategory_PAYM_30_HF
## # ℹ 6 more variables: PaymentCategory_PAYM_30_PN <chr>,
## # PaymentCategory_PAYM_90_HIP_KNEE <chr>, Payment_PAYM_30_AMI <chr>,
## # Payment_PAYM_30_HF <chr>, Payment_PAYM_30_PN <chr>,
## # Payment_PAYM_90_HIP_KNEE <chr>Joining and cleaning the datasets (AC)
Joining the datasets based on FacilityId
HipKneeClean <- readmissionsClean %>%
full_join(HCAHPSClean, by = "FacilityId") %>%
full_join(careClean, by = "FacilityId") %>%
full_join(deathsClean, by = "FacilityId") %>%
full_join(paymentClean, by = "FacilityId")
head(HipKneeClean)## # A tibble: 6 × 451
## FacilityName.x FacilityId State.x NumberOfDischarges_HIP-KN…¹
## <chr> <chr> <chr> <dbl>
## 1 SOUTHEAST HEALTH MEDICAL CENTER 010001 AL NA
## 2 MARSHALL MEDICAL CENTERS 010005 AL NA
## 3 NORTH ALABAMA MEDICAL CENTER 010006 AL NA
## 4 MIZELL MEMORIAL HOSPITAL 010007 AL NA
## 5 CRENSHAW COMMUNITY HOSPITAL 010008 AL NA
## 6 ST. VINCENT'S EAST 010011 AL NA
## # ℹ abbreviated name: ¹`NumberOfDischarges_HIP-KNEE`
## # ℹ 447 more variables: `ExcessReadmissionRatio_HIP-KNEE` <dbl>,
## # `PredictedReadmissionRate_HIP-KNEE` <dbl>,
## # `ExpectedReadmissionRate_HIP-KNEE` <dbl>,
## # `NumberOfReadmissions_HIP-KNEE` <dbl>, FacilityName.y <chr>, State.y <chr>,
## # PatientSurveyStarRating_H_COMP_1_A_P <chr>,
## # PatientSurveyStarRating_H_COMP_1_SN_P <chr>, …Removing redundant columns
# Removing duplicate columns
HipKneeClean <- HipKneeClean %>%
select(-matches("\\.(x|y|z|w|v)$"))Checking for NA Values
# Checking the dimensions
dim(HipKneeClean)
# Count NA values in each column
na_counts <- sapply(HipKneeClean, function(x) sum(is.na(x)))
# View the NA counts
print(na_counts)Removing columns with more than 80% NA values
# Calculate the percentage of NA values for each column
na_percentage <- sapply(HipKneeClean, function(x) mean(is.na(x)))
# Remove columns where more than 80% of the values are NA
HipKneeClean <- HipKneeClean[, na_percentage <= 0.8]
# Count NA values in each column
na_counts <- sapply(HipKneeClean, function(x) sum(is.na(x)))
# View the NA counts
print(na_counts)
# Check the dimensions
dim(HipKneeClean)Removing answer percent and survey response percent columns
# Remove columns containing 'AnswerPercent' or 'SurveyResponseRate'
HipKneeClean <- HipKneeClean %>%
select(-matches("AnswerPercent|SurveyResponseRate"))
# Check the dimensions
dim(HipKneeClean)## [1] 4816 87Removing compared to national columns
# Remove columns containing 'ComparedToNational' and 'PaymentCategory'
HipKneeClean <- HipKneeClean %>%
select(-matches("ComparedToNational|PaymentCategory"))
# Check the dimensions
dim(HipKneeClean)## [1] 4816 67Checking data structure
str(HipKneeClean)## tibble [4,816 × 67] (S3: tbl_df/tbl/data.frame)
## $ FacilityId : chr [1:4816] "010001" "010005" "010006" "010007" ...
## $ ExcessReadmissionRatio_HIP-KNEE : num [1:4816] 0.892 0.798 1.247 0.992 NA ...
## $ PredictedReadmissionRate_HIP-KNEE : num [1:4816] 3.53 3.76 5.52 4.34 NA ...
## $ ExpectedReadmissionRate_HIP-KNEE : num [1:4816] 3.96 4.72 4.43 4.37 NA ...
## $ NumberOfReadmissions_HIP-KNEE : num [1:4816] 8 1 9 9 NA 6 2 5 NA 9 ...
## $ PatientSurveyStarRating_H_COMP_1_STAR_RATING : chr [1:4816] "3" "3" "2" "3" ...
## $ PatientSurveyStarRating_H_COMP_2_STAR_RATING : chr [1:4816] "4" "4" "3" "5" ...
## $ PatientSurveyStarRating_H_COMP_3_STAR_RATING : chr [1:4816] "3" "2" "2" "4" ...
## $ PatientSurveyStarRating_H_COMP_5_STAR_RATING : chr [1:4816] "3" "3" "2" "3" ...
## $ PatientSurveyStarRating_H_COMP_6_STAR_RATING : chr [1:4816] "4" "3" "3" "4" ...
## $ PatientSurveyStarRating_H_COMP_7_STAR_RATING : chr [1:4816] "4" "3" "2" "4" ...
## $ PatientSurveyStarRating_H_CLEAN_STAR_RATING : chr [1:4816] "3" "2" "1" "2" ...
## $ PatientSurveyStarRating_H_QUIET_STAR_RATING : chr [1:4816] "4" "4" "4" "4" ...
## $ PatientSurveyStarRating_H_HSP_RATING_STAR_RATING: chr [1:4816] "4" "3" "2" "4" ...
## $ PatientSurveyStarRating_H_RECMND_STAR_RATING : chr [1:4816] "4" "3" "2" "4" ...
## $ PatientSurveyStarRating_H_STAR_RATING : chr [1:4816] "4" "3" "2" "4" ...
## $ HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE : chr [1:4816] "89" "90" "88" "91" ...
## $ HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE : chr [1:4816] "91" "92" "89" "95" ...
## $ HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE : chr [1:4816] "81" "75" "75" "88" ...
## $ HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE : chr [1:4816] "77" "76" "71" "77" ...
## $ HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE : chr [1:4816] "87" "86" "83" "87" ...
## $ HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE : chr [1:4816] "82" "79" "77" "82" ...
## $ HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE : chr [1:4816] "84" "80" "74" "80" ...
## $ HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE : chr [1:4816] "86" "85" "85" "87" ...
## $ HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE : chr [1:4816] "89" "85" "82" "89" ...
## $ HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE : chr [1:4816] "90" "83" "79" "88" ...
## $ EDV : chr [1:4816] "high" "high" "high" "low" ...
## $ ED_2_Strata_1 : chr [1:4816] NA "148" NA NA ...
## $ HCP_COVID_19 : chr [1:4816] "80.7" "79.8" "79" "57.9" ...
## $ IMM_3 : chr [1:4816] "95" "80" "67" "53" ...
## $ OP_18b : chr [1:4816] "215" "147" "177" "130" ...
## $ OP_18c : chr [1:4816] "317" "266" NA "216" ...
## $ OP_22 : chr [1:4816] "5" "3" "1" "4" ...
## $ OP_23 : chr [1:4816] NA NA "69" NA ...
## $ OP_29 : chr [1:4816] "47" "96" "85" "23" ...
## $ SAFE_USE_OF_OPIOIDS : chr [1:4816] "14" "19" "17" NA ...
## $ SEP_1 : chr [1:4816] "66" "74" "56" "86" ...
## $ SEP_SH_3HR : chr [1:4816] "70" "88" "77" NA ...
## $ SEP_SH_6HR : chr [1:4816] "100" "91" "81" NA ...
## $ SEV_SEP_3HR : chr [1:4816] "79" "88" "78" "89" ...
## $ SEV_SEP_6HR : chr [1:4816] "95" "96" "86" "97" ...
## $ STK_02 : chr [1:4816] "98" "100" "96" NA ...
## $ STK_05 : chr [1:4816] NA "91" NA NA ...
## $ STK_06 : chr [1:4816] NA NA "97" NA ...
## $ VTE_1 : chr [1:4816] "98" NA NA NA ...
## $ VTE_2 : chr [1:4816] "99" NA "97" NA ...
## $ Score_COMP_HIP_KNEE : chr [1:4816] "2.7" "2.3" "4.6" NA ...
## $ Score_MORT_30_AMI : chr [1:4816] "12" "13.6" "16.5" NA ...
## $ Score_MORT_30_COPD : chr [1:4816] "8.8" "9.9" "9.9" "13.7" ...
## $ Score_MORT_30_HF : chr [1:4816] "8.9" "14.9" "12.5" "12.5" ...
## $ Score_MORT_30_PN : chr [1:4816] "18" "23.3" "19.5" "28.5" ...
## $ Score_MORT_30_STK : chr [1:4816] "14.8" "15.3" "17.2" NA ...
## $ Score_PSI_03 : chr [1:4816] "0.39" "0.94" "1.39" "0.42" ...
## $ Score_PSI_04 : chr [1:4816] "184.68" "183.49" "173.63" NA ...
## $ Score_PSI_06 : chr [1:4816] "0.23" "0.22" "0.36" "0.24" ...
## $ Score_PSI_08 : chr [1:4816] "0.10" "0.09" "0.08" "0.09" ...
## $ Score_PSI_09 : chr [1:4816] "2.39" "2.69" "5.43" "2.49" ...
## $ Score_PSI_10 : chr [1:4816] "1.14" "1.37" "1.26" "1.57" ...
## $ Score_PSI_11 : chr [1:4816] "13.83" "7.19" "7.37" "8.45" ...
## $ Score_PSI_12 : chr [1:4816] "4.49" "3.01" "3.36" "3.89" ...
## $ Score_PSI_13 : chr [1:4816] "8.05" "4.46" "4.37" "5.19" ...
## $ Score_PSI_14 : chr [1:4816] "1.69" "1.87" "1.76" NA ...
## $ Score_PSI_15 : chr [1:4816] "0.93" "0.91" "1.34" "1.08" ...
## $ Score_PSI_90 : chr [1:4816] "1.21" "0.97" "1.17" "0.95" ...
## $ FacilityName : chr [1:4816] "SOUTHEAST HEALTH MEDICAL CENTER" "MARSHALL MEDICAL CENTERS" "NORTH ALABAMA MEDICAL CENTER" "MIZELL MEMORIAL HOSPITAL" ...
## $ State : chr [1:4816] "AL" "AL" "AL" "AL" ...
## $ Payment_PAYM_90_HIP_KNEE : chr [1:4816] "$22,212" "$18,030" "$21,898" NA ...# Convert columns to numeric
HipKneeClean <- HipKneeClean %>%
mutate_at(vars(starts_with("PatientSurveyStarRating_"),
starts_with("HcahpsLinearMeanValue_"),
starts_with("Score_"),
starts_with("ED_"),
starts_with("IMM_"),
starts_with("OP_"),
starts_with("SEP_"),
starts_with("SEV_"),
starts_with("STK_"),
starts_with("VTE_"),
starts_with("SAFE_"),
starts_with("HCP_")),
~ as.numeric(as.character(.)))
# View the structure
str(HipKneeClean)## tibble [4,816 × 67] (S3: tbl_df/tbl/data.frame)
## $ FacilityId : chr [1:4816] "010001" "010005" "010006" "010007" ...
## $ ExcessReadmissionRatio_HIP-KNEE : num [1:4816] 0.892 0.798 1.247 0.992 NA ...
## $ PredictedReadmissionRate_HIP-KNEE : num [1:4816] 3.53 3.76 5.52 4.34 NA ...
## $ ExpectedReadmissionRate_HIP-KNEE : num [1:4816] 3.96 4.72 4.43 4.37 NA ...
## $ NumberOfReadmissions_HIP-KNEE : num [1:4816] 8 1 9 9 NA 6 2 5 NA 9 ...
## $ PatientSurveyStarRating_H_COMP_1_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_2_STAR_RATING : num [1:4816] 4 4 3 5 NA 3 4 4 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_3_STAR_RATING : num [1:4816] 3 2 2 4 NA 4 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_5_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_6_STAR_RATING : num [1:4816] 4 3 3 4 NA 3 3 2 NA 3 ...
## $ PatientSurveyStarRating_H_COMP_7_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_CLEAN_STAR_RATING : num [1:4816] 3 2 1 2 NA 2 2 1 NA 4 ...
## $ PatientSurveyStarRating_H_QUIET_STAR_RATING : num [1:4816] 4 4 4 4 NA 4 4 3 NA 5 ...
## $ PatientSurveyStarRating_H_HSP_RATING_STAR_RATING: num [1:4816] 4 3 2 4 NA 3 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_RECMND_STAR_RATING : num [1:4816] 4 3 2 4 NA 4 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE : num [1:4816] 89 90 88 91 NA 90 91 89 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE : num [1:4816] 91 92 89 95 NA 90 91 91 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE : num [1:4816] 81 75 75 88 NA 85 80 78 NA 85 ...
## $ HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE : num [1:4816] 77 76 71 77 NA 76 76 72 NA 78 ...
## $ HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE : num [1:4816] 87 86 83 87 NA 86 86 81 NA 86 ...
## $ HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE : num [1:4816] 82 79 77 82 NA 81 79 80 NA 83 ...
## $ HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE : num [1:4816] 84 80 74 80 NA 81 83 78 NA 88 ...
## $ HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE : num [1:4816] 86 85 85 87 NA 84 84 82 NA 89 ...
## $ HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE : num [1:4816] 89 85 82 89 NA 88 83 85 NA 90 ...
## $ HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE : num [1:4816] 90 83 79 88 NA 87 80 84 NA 91 ...
## $ EDV : chr [1:4816] "high" "high" "high" "low" ...
## $ ED_2_Strata_1 : num [1:4816] NA 148 NA NA NA NA NA NA NA NA ...
## $ HCP_COVID_19 : num [1:4816] 80.7 79.8 79 57.9 81.2 88 69.8 87.3 95.9 85.3 ...
## $ IMM_3 : num [1:4816] 95 80 67 53 45 81 65 93 98 81 ...
## $ OP_18b : num [1:4816] 215 147 177 130 118 206 160 185 102 145 ...
## $ OP_18c : num [1:4816] 317 266 NA 216 98 124 220 220 NA 324 ...
## $ OP_22 : num [1:4816] 5 3 1 4 0 5 4 3 0 2 ...
## $ OP_23 : num [1:4816] NA NA 69 NA NA 47 NA 73 NA 35 ...
## $ OP_29 : num [1:4816] 47 96 85 23 67 100 100 NA NA 82 ...
## $ SAFE_USE_OF_OPIOIDS : num [1:4816] 14 19 17 NA NA 20 14 23 NA 17 ...
## $ SEP_1 : num [1:4816] 66 74 56 86 NA 51 92 77 NA 87 ...
## $ SEP_SH_3HR : num [1:4816] 70 88 77 NA NA 78 94 83 NA 90 ...
## $ SEP_SH_6HR : num [1:4816] 100 91 81 NA NA 81 83 100 NA 94 ...
## $ SEV_SEP_3HR : num [1:4816] 79 88 78 89 NA 69 95 85 NA 94 ...
## $ SEV_SEP_6HR : num [1:4816] 95 96 86 97 NA 91 99 97 NA 99 ...
## $ STK_02 : num [1:4816] 98 100 96 NA NA 93 NA 99 NA NA ...
## $ STK_05 : num [1:4816] NA 91 NA NA NA NA NA NA NA NA ...
## $ STK_06 : num [1:4816] NA NA 97 NA NA NA NA NA NA NA ...
## $ VTE_1 : num [1:4816] 98 NA NA NA NA 79 89 84 44 59 ...
## $ VTE_2 : num [1:4816] 99 NA 97 NA NA 88 93 94 NA NA ...
## $ Score_COMP_HIP_KNEE : num [1:4816] 2.7 2.3 4.6 NA NA 3.5 3.8 3.5 NA 4.3 ...
## $ Score_MORT_30_AMI : num [1:4816] 12 13.6 16.5 NA NA 13.2 13.8 13.1 NA NA ...
## $ Score_MORT_30_COPD : num [1:4816] 8.8 9.9 9.9 13.7 NA 10.3 NA 9.2 NA 7.8 ...
## $ Score_MORT_30_HF : num [1:4816] 8.9 14.9 12.5 12.5 NA 13.5 13.6 9.9 NA 16.9 ...
## $ Score_MORT_30_PN : num [1:4816] 18 23.3 19.5 28.5 NA 20.9 22 17.2 NA 26.1 ...
## $ Score_MORT_30_STK : num [1:4816] 14.8 15.3 17.2 NA NA 12.3 NA 13.2 NA 17.3 ...
## $ Score_PSI_03 : num [1:4816] 0.39 0.94 1.39 0.42 0.54 0.13 0.41 0.63 0.57 0.47 ...
## $ Score_PSI_04 : num [1:4816] 185 183 174 NA NA ...
## $ Score_PSI_06 : num [1:4816] 0.23 0.22 0.36 0.24 0.25 0.24 0.24 0.21 0.25 0.22 ...
## $ Score_PSI_08 : num [1:4816] 0.1 0.09 0.08 0.09 0.09 0.08 0.09 0.09 0.09 0.09 ...
## $ Score_PSI_09 : num [1:4816] 2.39 2.69 5.43 2.49 NA 1.88 2.44 3.29 2.44 2.58 ...
## $ Score_PSI_10 : num [1:4816] 1.14 1.37 1.26 1.57 NA 1.72 1.51 1.2 1.57 NA ...
## $ Score_PSI_11 : num [1:4816] 13.83 7.19 7.37 8.45 NA ...
## $ Score_PSI_12 : num [1:4816] 4.49 3.01 3.36 3.89 NA 3.04 3.32 3.67 3.56 5.63 ...
## $ Score_PSI_13 : num [1:4816] 8.05 4.46 4.37 5.19 NA 5.55 4.88 6.08 5.18 NA ...
## $ Score_PSI_14 : num [1:4816] 1.69 1.87 1.76 NA NA 1.86 2.46 2.77 NA 1.83 ...
## $ Score_PSI_15 : num [1:4816] 0.93 0.91 1.34 1.08 NA 1.18 1.04 0.84 NA 0.88 ...
## $ Score_PSI_90 : num [1:4816] 1.21 0.97 1.17 0.95 NA 0.72 0.89 1.17 0.98 1.05 ...
## $ FacilityName : chr [1:4816] "SOUTHEAST HEALTH MEDICAL CENTER" "MARSHALL MEDICAL CENTERS" "NORTH ALABAMA MEDICAL CENTER" "MIZELL MEMORIAL HOSPITAL" ...
## $ State : chr [1:4816] "AL" "AL" "AL" "AL" ...
## $ Payment_PAYM_90_HIP_KNEE : chr [1:4816] "$22,212" "$18,030" "$21,898" NA ...Fixing the payment column
# Remove $ and , and convert to numeric
HipKneeClean <- HipKneeClean %>%
mutate_at(vars(starts_with("Payment_")),
~ as.numeric(gsub("[\\$,]", "", .)))
# Checking the structure
str(HipKneeClean)## tibble [4,816 × 67] (S3: tbl_df/tbl/data.frame)
## $ FacilityId : chr [1:4816] "010001" "010005" "010006" "010007" ...
## $ ExcessReadmissionRatio_HIP-KNEE : num [1:4816] 0.892 0.798 1.247 0.992 NA ...
## $ PredictedReadmissionRate_HIP-KNEE : num [1:4816] 3.53 3.76 5.52 4.34 NA ...
## $ ExpectedReadmissionRate_HIP-KNEE : num [1:4816] 3.96 4.72 4.43 4.37 NA ...
## $ NumberOfReadmissions_HIP-KNEE : num [1:4816] 8 1 9 9 NA 6 2 5 NA 9 ...
## $ PatientSurveyStarRating_H_COMP_1_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_2_STAR_RATING : num [1:4816] 4 4 3 5 NA 3 4 4 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_3_STAR_RATING : num [1:4816] 3 2 2 4 NA 4 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_5_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_6_STAR_RATING : num [1:4816] 4 3 3 4 NA 3 3 2 NA 3 ...
## $ PatientSurveyStarRating_H_COMP_7_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_CLEAN_STAR_RATING : num [1:4816] 3 2 1 2 NA 2 2 1 NA 4 ...
## $ PatientSurveyStarRating_H_QUIET_STAR_RATING : num [1:4816] 4 4 4 4 NA 4 4 3 NA 5 ...
## $ PatientSurveyStarRating_H_HSP_RATING_STAR_RATING: num [1:4816] 4 3 2 4 NA 3 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_RECMND_STAR_RATING : num [1:4816] 4 3 2 4 NA 4 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE : num [1:4816] 89 90 88 91 NA 90 91 89 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE : num [1:4816] 91 92 89 95 NA 90 91 91 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE : num [1:4816] 81 75 75 88 NA 85 80 78 NA 85 ...
## $ HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE : num [1:4816] 77 76 71 77 NA 76 76 72 NA 78 ...
## $ HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE : num [1:4816] 87 86 83 87 NA 86 86 81 NA 86 ...
## $ HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE : num [1:4816] 82 79 77 82 NA 81 79 80 NA 83 ...
## $ HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE : num [1:4816] 84 80 74 80 NA 81 83 78 NA 88 ...
## $ HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE : num [1:4816] 86 85 85 87 NA 84 84 82 NA 89 ...
## $ HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE : num [1:4816] 89 85 82 89 NA 88 83 85 NA 90 ...
## $ HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE : num [1:4816] 90 83 79 88 NA 87 80 84 NA 91 ...
## $ EDV : chr [1:4816] "high" "high" "high" "low" ...
## $ ED_2_Strata_1 : num [1:4816] NA 148 NA NA NA NA NA NA NA NA ...
## $ HCP_COVID_19 : num [1:4816] 80.7 79.8 79 57.9 81.2 88 69.8 87.3 95.9 85.3 ...
## $ IMM_3 : num [1:4816] 95 80 67 53 45 81 65 93 98 81 ...
## $ OP_18b : num [1:4816] 215 147 177 130 118 206 160 185 102 145 ...
## $ OP_18c : num [1:4816] 317 266 NA 216 98 124 220 220 NA 324 ...
## $ OP_22 : num [1:4816] 5 3 1 4 0 5 4 3 0 2 ...
## $ OP_23 : num [1:4816] NA NA 69 NA NA 47 NA 73 NA 35 ...
## $ OP_29 : num [1:4816] 47 96 85 23 67 100 100 NA NA 82 ...
## $ SAFE_USE_OF_OPIOIDS : num [1:4816] 14 19 17 NA NA 20 14 23 NA 17 ...
## $ SEP_1 : num [1:4816] 66 74 56 86 NA 51 92 77 NA 87 ...
## $ SEP_SH_3HR : num [1:4816] 70 88 77 NA NA 78 94 83 NA 90 ...
## $ SEP_SH_6HR : num [1:4816] 100 91 81 NA NA 81 83 100 NA 94 ...
## $ SEV_SEP_3HR : num [1:4816] 79 88 78 89 NA 69 95 85 NA 94 ...
## $ SEV_SEP_6HR : num [1:4816] 95 96 86 97 NA 91 99 97 NA 99 ...
## $ STK_02 : num [1:4816] 98 100 96 NA NA 93 NA 99 NA NA ...
## $ STK_05 : num [1:4816] NA 91 NA NA NA NA NA NA NA NA ...
## $ STK_06 : num [1:4816] NA NA 97 NA NA NA NA NA NA NA ...
## $ VTE_1 : num [1:4816] 98 NA NA NA NA 79 89 84 44 59 ...
## $ VTE_2 : num [1:4816] 99 NA 97 NA NA 88 93 94 NA NA ...
## $ Score_COMP_HIP_KNEE : num [1:4816] 2.7 2.3 4.6 NA NA 3.5 3.8 3.5 NA 4.3 ...
## $ Score_MORT_30_AMI : num [1:4816] 12 13.6 16.5 NA NA 13.2 13.8 13.1 NA NA ...
## $ Score_MORT_30_COPD : num [1:4816] 8.8 9.9 9.9 13.7 NA 10.3 NA 9.2 NA 7.8 ...
## $ Score_MORT_30_HF : num [1:4816] 8.9 14.9 12.5 12.5 NA 13.5 13.6 9.9 NA 16.9 ...
## $ Score_MORT_30_PN : num [1:4816] 18 23.3 19.5 28.5 NA 20.9 22 17.2 NA 26.1 ...
## $ Score_MORT_30_STK : num [1:4816] 14.8 15.3 17.2 NA NA 12.3 NA 13.2 NA 17.3 ...
## $ Score_PSI_03 : num [1:4816] 0.39 0.94 1.39 0.42 0.54 0.13 0.41 0.63 0.57 0.47 ...
## $ Score_PSI_04 : num [1:4816] 185 183 174 NA NA ...
## $ Score_PSI_06 : num [1:4816] 0.23 0.22 0.36 0.24 0.25 0.24 0.24 0.21 0.25 0.22 ...
## $ Score_PSI_08 : num [1:4816] 0.1 0.09 0.08 0.09 0.09 0.08 0.09 0.09 0.09 0.09 ...
## $ Score_PSI_09 : num [1:4816] 2.39 2.69 5.43 2.49 NA 1.88 2.44 3.29 2.44 2.58 ...
## $ Score_PSI_10 : num [1:4816] 1.14 1.37 1.26 1.57 NA 1.72 1.51 1.2 1.57 NA ...
## $ Score_PSI_11 : num [1:4816] 13.83 7.19 7.37 8.45 NA ...
## $ Score_PSI_12 : num [1:4816] 4.49 3.01 3.36 3.89 NA 3.04 3.32 3.67 3.56 5.63 ...
## $ Score_PSI_13 : num [1:4816] 8.05 4.46 4.37 5.19 NA 5.55 4.88 6.08 5.18 NA ...
## $ Score_PSI_14 : num [1:4816] 1.69 1.87 1.76 NA NA 1.86 2.46 2.77 NA 1.83 ...
## $ Score_PSI_15 : num [1:4816] 0.93 0.91 1.34 1.08 NA 1.18 1.04 0.84 NA 0.88 ...
## $ Score_PSI_90 : num [1:4816] 1.21 0.97 1.17 0.95 NA 0.72 0.89 1.17 0.98 1.05 ...
## $ FacilityName : chr [1:4816] "SOUTHEAST HEALTH MEDICAL CENTER" "MARSHALL MEDICAL CENTERS" "NORTH ALABAMA MEDICAL CENTER" "MIZELL MEMORIAL HOSPITAL" ...
## $ State : chr [1:4816] "AL" "AL" "AL" "AL" ...
## $ Payment_PAYM_90_HIP_KNEE : num [1:4816] 22212 18030 21898 NA NA ...Saving the data to use without having to clean it
save(HipKneeClean, file = "HipKneeClean.RData")Exploring the cleaned data
Generating visualizations (SE)
Creating a summary table for numeric variables
# Select numeric columns
numeric_columns <- select_if(HipKneeClean, is.numeric)
# Calculate descriptive statistics
descr_stats <- psych::describe(numeric_columns)
# Convert to a data frame
descr_stats_df <- as.data.frame(descr_stats)
# Display the table
kable(descr_stats_df, format = "html", caption = "Table 6. Descriptive Statistics for Numeric Variables in Cleaned Dataset") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ExcessReadmissionRatio_HIP-KNEE | 1 | 1838 | 1.004161e+00 | 0.1263979 | 0.9921 | 1.000079e+00 | 0.1119363 | 0.6159 | 1.5162 | 0.9003 | 0.3862731 | 0.7447069 | 0.0029483 |
| PredictedReadmissionRate_HIP-KNEE | 2 | 1838 | 4.546552e+00 | 0.9092848 | 4.4768 | 4.511130e+00 | 0.8590184 | 1.9279 | 8.5690 | 6.6411 | 0.4370579 | 0.4866461 | 0.0212093 |
| ExpectedReadmissionRate_HIP-KNEE | 3 | 1838 | 4.519903e+00 | 0.6637697 | 4.4544 | 4.484779e+00 | 0.6165392 | 2.6749 | 7.6240 | 4.9491 | 0.6361300 | 1.0010780 | 0.0154826 |
| NumberOfReadmissions_HIP-KNEE | 4 | 1838 | 8.127312e+00 | 7.8319381 | 7.0000 | 6.852582e+00 | 4.4478000 | 1.0000 | 125.0000 | 124.0000 | 4.5229809 | 40.5503377 | 0.1826823 |
| PatientSurveyStarRating_H_COMP_1_STAR_RATING | 5 | 3255 | 3.260215e+00 | 1.0059133 | 3.0000 | 3.241843e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | 0.0239346 | -0.4825494 | 0.0176313 |
| PatientSurveyStarRating_H_COMP_2_STAR_RATING | 6 | 3255 | 3.428264e+00 | 0.9474515 | 3.0000 | 3.450672e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.3484208 | -0.0771131 | 0.0166066 |
| PatientSurveyStarRating_H_COMP_3_STAR_RATING | 7 | 3255 | 3.372350e+00 | 1.0909348 | 4.0000 | 3.388100e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.2839572 | -0.8381418 | 0.0191216 |
| PatientSurveyStarRating_H_COMP_5_STAR_RATING | 8 | 3255 | 3.064516e+00 | 0.9126664 | 3.0000 | 3.062572e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.0135291 | -0.3800413 | 0.0159969 |
| PatientSurveyStarRating_H_COMP_6_STAR_RATING | 9 | 3255 | 3.388940e+00 | 0.9148777 | 3.0000 | 3.401919e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.3167335 | 0.0370958 | 0.0160357 |
| PatientSurveyStarRating_H_COMP_7_STAR_RATING | 10 | 3255 | 3.167742e+00 | 0.9963691 | 3.0000 | 3.144338e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.0232912 | -0.4736871 | 0.0174640 |
| PatientSurveyStarRating_H_CLEAN_STAR_RATING | 11 | 3255 | 3.049770e+00 | 1.1197420 | 3.0000 | 3.063724e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.1031912 | -0.6984520 | 0.0196265 |
| PatientSurveyStarRating_H_QUIET_STAR_RATING | 12 | 3255 | 3.214132e+00 | 1.1166932 | 3.0000 | 3.228791e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.1265578 | -0.6910097 | 0.0195730 |
| PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | 13 | 3255 | 3.243318e+00 | 0.9195166 | 3.0000 | 3.268330e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.2786674 | 0.0381366 | 0.0161170 |
| PatientSurveyStarRating_H_RECMND_STAR_RATING | 14 | 3255 | 3.497696e+00 | 1.0287408 | 4.0000 | 3.554702e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.6160258 | -0.1587475 | 0.0180314 |
| PatientSurveyStarRating_H_STAR_RATING | 15 | 3255 | 3.295545e+00 | 0.9142197 | 3.0000 | 3.294818e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.1485226 | -0.2457959 | 0.0160242 |
| HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | 16 | 3255 | 9.049002e+01 | 2.9117012 | 91.0000 | 9.063608e+01 | 2.9652000 | 77.0000 | 100.0000 | 23.0000 | -0.6770638 | 1.4141442 | 0.0510354 |
| HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | 17 | 3255 | 9.028879e+01 | 2.8233453 | 90.0000 | 9.039079e+01 | 2.9652000 | 76.0000 | 100.0000 | 24.0000 | -0.4886223 | 1.1133723 | 0.0494867 |
| HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | 18 | 3255 | 8.276897e+01 | 5.3062993 | 83.0000 | 8.280499e+01 | 4.4478000 | 61.0000 | 100.0000 | 39.0000 | -0.1793486 | 0.4429645 | 0.0930071 |
| HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | 19 | 3255 | 7.554593e+01 | 5.1600947 | 75.0000 | 7.547178e+01 | 4.4478000 | 51.0000 | 99.0000 | 48.0000 | 0.0989232 | 0.6200355 | 0.0904445 |
| HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | 20 | 3255 | 8.568786e+01 | 4.1729980 | 86.0000 | 8.596238e+01 | 2.9652000 | 59.0000 | 100.0000 | 41.0000 | -0.9067227 | 2.2178795 | 0.0731430 |
| HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | 21 | 3255 | 8.040277e+01 | 3.2911839 | 81.0000 | 8.046795e+01 | 2.9652000 | 64.0000 | 97.0000 | 33.0000 | -0.2792695 | 1.0404404 | 0.0576868 |
| HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | 22 | 3255 | 8.560799e+01 | 4.7038213 | 86.0000 | 8.575931e+01 | 4.4478000 | 68.0000 | 99.0000 | 31.0000 | -0.3531253 | 0.1936870 | 0.0824471 |
| HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | 23 | 3255 | 8.172657e+01 | 5.6927220 | 82.0000 | 8.193282e+01 | 5.9304000 | 56.0000 | 99.0000 | 43.0000 | -0.3843168 | 0.2536999 | 0.0997802 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 24 | 3255 | 8.709708e+01 | 4.0649053 | 88.0000 | 8.730940e+01 | 2.9652000 | 65.0000 | 98.0000 | 33.0000 | -0.6979236 | 1.3325347 | 0.0712484 |
| HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | 25 | 3255 | 8.607343e+01 | 5.2109599 | 87.0000 | 8.638349e+01 | 4.4478000 | 57.0000 | 99.0000 | 42.0000 | -0.7407627 | 1.2385658 | 0.0913361 |
| ED_2_Strata_1 | 26 | 1107 | 1.064002e+02 | 114.0608548 | 74.0000 | 8.602368e+01 | 50.4084000 | 0.0000 | 1078.0000 | 1078.0000 | 3.9796326 | 22.8953094 | 3.4281736 |
| HCP_COVID_19 | 27 | 3633 | 8.767556e+01 | 10.6218376 | 90.1000 | 8.903068e+01 | 9.4886400 | 0.5000 | 100.0000 | 99.5000 | -1.4380632 | 3.5511860 | 0.1762248 |
| IMM_3 | 28 | 4140 | 7.782681e+01 | 18.5753061 | 83.0000 | 8.024245e+01 | 17.7912000 | 0.0000 | 100.0000 | 100.0000 | -1.0622115 | 0.7180171 | 0.2886927 |
| OP_18b | 29 | 4067 | 1.617780e+02 | 54.6367977 | 153.0000 | 1.572833e+02 | 53.3736000 | 38.0000 | 587.0000 | 549.0000 | 0.9959452 | 2.0284358 | 0.8567382 |
| OP_18c | 30 | 3098 | 2.967434e+02 | 177.2966416 | 255.0000 | 2.700827e+02 | 117.1254000 | 40.0000 | 2954.0000 | 2914.0000 | 3.5082626 | 28.6044027 | 3.1853694 |
| OP_22 | 31 | 3841 | 2.385056e+00 | 2.3270098 | 2.0000 | 2.028637e+00 | 1.4826000 | 0.0000 | 19.0000 | 19.0000 | 1.7797727 | 4.6722341 | 0.0375471 |
| OP_23 | 32 | 1535 | 7.062801e+01 | 19.2269197 | 74.0000 | 7.247193e+01 | 17.7912000 | 0.0000 | 100.0000 | 100.0000 | -0.9377173 | 0.8863709 | 0.4907446 |
| OP_29 | 33 | 2830 | 9.125230e+01 | 14.2952983 | 96.0000 | 9.458083e+01 | 5.9304000 | 0.0000 | 100.0000 | 100.0000 | -3.1390450 | 11.9252917 | 0.2687200 |
| SAFE_USE_OF_OPIOIDS | 34 | 3670 | 1.561226e+01 | 5.6808277 | 15.0000 | 1.537568e+01 | 4.4478000 | 0.0000 | 45.0000 | 45.0000 | 0.6530895 | 2.0207900 | 0.0937732 |
| SEP_1 | 35 | 3097 | 5.982661e+01 | 16.7144073 | 61.0000 | 6.045180e+01 | 16.3086000 | 0.0000 | 100.0000 | 100.0000 | -0.4029034 | 0.1050294 | 0.3003450 |
| SEP_SH_3HR | 36 | 2620 | 6.724809e+01 | 17.8935243 | 68.0000 | 6.776813e+01 | 19.2738000 | 0.0000 | 100.0000 | 100.0000 | -0.2914787 | -0.2730680 | 0.3495789 |
| SEP_SH_6HR | 37 | 2039 | 8.305983e+01 | 15.4244924 | 87.0000 | 8.529455e+01 | 11.8608000 | 7.0000 | 100.0000 | 93.0000 | -1.5023285 | 2.7030480 | 0.3415877 |
| SEV_SEP_3HR | 38 | 3086 | 7.904342e+01 | 11.1773414 | 81.0000 | 7.998907e+01 | 10.3782000 | 0.0000 | 100.0000 | 100.0000 | -1.4060736 | 4.9567276 | 0.2012058 |
| SEV_SEP_6HR | 39 | 2937 | 8.871263e+01 | 11.3390205 | 92.0000 | 9.061974e+01 | 7.4130000 | 0.0000 | 100.0000 | 100.0000 | -2.4051707 | 8.9993568 | 0.2092298 |
| STK_02 | 40 | 1537 | 9.529733e+01 | 6.1635225 | 97.0000 | 9.638262e+01 | 2.9652000 | 23.0000 | 100.0000 | 77.0000 | -4.7304997 | 35.8413538 | 0.1572143 |
| STK_05 | 41 | 1094 | 9.278702e+01 | 7.1402994 | 94.0000 | 9.367352e+01 | 4.4478000 | 2.0000 | 100.0000 | 98.0000 | -5.4512524 | 54.5350543 | 0.2158777 |
| STK_06 | 42 | 1298 | 9.464946e+01 | 7.5064354 | 96.0000 | 9.581154e+01 | 2.9652000 | 0.0000 | 100.0000 | 100.0000 | -7.3728956 | 77.6048723 | 0.2083514 |
| VTE_1 | 43 | 2216 | 8.246435e+01 | 19.1503872 | 89.0000 | 8.603777e+01 | 11.8608000 | 0.0000 | 100.0000 | 100.0000 | -1.7514646 | 3.0387995 | 0.4068110 |
| VTE_2 | 44 | 1413 | 9.383015e+01 | 9.7362977 | 97.0000 | 9.588241e+01 | 2.9652000 | 3.0000 | 100.0000 | 97.0000 | -4.1429487 | 23.6985405 | 0.2590137 |
| Score_COMP_HIP_KNEE | 45 | 2090 | 3.182392e+00 | 0.5482694 | 3.1000 | 3.150419e+00 | 0.4447800 | 1.6000 | 6.2000 | 4.6000 | 0.7716603 | 1.9037431 | 0.0119928 |
| Score_MORT_30_AMI | 46 | 1943 | 1.254359e+01 | 1.1553168 | 12.5000 | 1.251608e+01 | 1.0378200 | 8.9000 | 17.1000 | 8.2000 | 0.2785565 | 0.5897728 | 0.0262099 |
| Score_MORT_30_COPD | 47 | 2569 | 9.185286e+00 | 1.3614554 | 9.1000 | 9.121196e+00 | 1.3343400 | 5.2000 | 14.9000 | 9.7000 | 0.5044944 | 0.5326934 | 0.0268609 |
| Score_MORT_30_HF | 48 | 3056 | 1.182863e+01 | 1.9384358 | 11.8000 | 1.180581e+01 | 1.7791200 | 5.5000 | 20.4000 | 14.9000 | 0.1359787 | 0.4028740 | 0.0350651 |
| Score_MORT_30_PN | 49 | 3514 | 1.833056e+01 | 2.5441335 | 18.2000 | 1.826543e+01 | 2.3721600 | 8.6000 | 29.5000 | 20.9000 | 0.3130182 | 0.5748975 | 0.0429180 |
| Score_MORT_30_STK | 50 | 2123 | 1.379157e+01 | 1.8194129 | 13.7000 | 1.371648e+01 | 1.7791200 | 8.0000 | 21.9000 | 13.9000 | 0.4400162 | 0.5676934 | 0.0394872 |
| Score_PSI_03 | 51 | 3169 | 5.805491e-01 | 0.4702323 | 0.4800 | 5.037288e-01 | 0.2372160 | 0.0500 | 6.3100 | 6.2600 | 4.0520349 | 30.4735061 | 0.0083532 |
| Score_PSI_04 | 52 | 1609 | 1.687290e+02 | 21.3153769 | 167.7400 | 1.687267e+02 | 20.2523160 | 86.6800 | 241.8100 | 155.1300 | -0.0315953 | 0.4882789 | 0.5313920 |
| Score_PSI_06 | 53 | 3188 | 2.476851e-01 | 0.0402023 | 0.2400 | 2.442712e-01 | 0.0296520 | 0.1200 | 0.5100 | 0.3900 | 1.1937679 | 3.4906535 | 0.0007120 |
| Score_PSI_08 | 54 | 3189 | 9.043270e-02 | 0.0070889 | 0.0900 | 9.019980e-02 | 0.0000000 | 0.0600 | 0.1300 | 0.0700 | 0.5878153 | 2.8393103 | 0.0001255 |
| Score_PSI_09 | 55 | 2930 | 2.508707e+00 | 0.4395922 | 2.4600 | 2.478486e+00 | 0.2668680 | 1.1000 | 6.1000 | 5.0000 | 1.3622221 | 5.9305415 | 0.0081211 |
| Score_PSI_10 | 56 | 2593 | 1.569626e+00 | 0.3418816 | 1.5300 | 1.535055e+00 | 0.1186080 | 0.4700 | 4.5500 | 4.0800 | 1.9801686 | 8.7853292 | 0.0067139 |
| Score_PSI_11 | 57 | 2603 | 9.045517e+00 | 3.2148329 | 8.3900 | 8.740322e+00 | 2.1201180 | 2.7300 | 66.8500 | 64.1200 | 4.3362544 | 54.8289666 | 0.0630117 |
| Score_PSI_12 | 58 | 2935 | 3.597278e+00 | 0.7194093 | 3.5000 | 3.542005e+00 | 0.5633880 | 1.6100 | 7.5100 | 5.9000 | 1.0157663 | 2.2968831 | 0.0132792 |
| Score_PSI_13 | 59 | 2549 | 5.298133e+00 | 0.9887454 | 5.1300 | 5.224669e+00 | 0.7116480 | 2.1700 | 13.4900 | 11.3200 | 1.1662305 | 4.3263395 | 0.0195839 |
| Score_PSI_14 | 60 | 2592 | 2.010590e+00 | 0.3338405 | 1.9400 | 1.969769e+00 | 0.1482600 | 0.8900 | 4.4000 | 3.5100 | 1.9779060 | 7.1675818 | 0.0065572 |
| Score_PSI_15 | 61 | 2916 | 1.101708e+00 | 0.2939729 | 1.0500 | 1.067549e+00 | 0.1630860 | 0.3500 | 3.4300 | 3.0800 | 1.8219347 | 6.3874487 | 0.0054439 |
| Score_PSI_90 | 62 | 3011 | 1.001588e+00 | 0.1793301 | 0.9700 | 9.839477e-01 | 0.1186080 | 0.5500 | 2.7400 | 2.1900 | 2.0610890 | 10.6309961 | 0.0032681 |
| Payment_PAYM_90_HIP_KNEE | 63 | 2001 | 2.105813e+04 | 2079.2072318 | 20899.0000 | 2.093031e+04 | 1756.8810000 | 15936.0000 | 48153.0000 | 32217.0000 | 1.7757439 | 15.5385065 | 46.4808683 |
Exploring categorical variables
# Visualizing the distribution of EDV (Emergency Department Volume)
ggplot(HipKneeClean, aes(x = EDV)) +
geom_bar(fill = "skyblue", color = "black", alpha = 0.7) +
labs(title = "Figure 1. Distribution of Emergency Department Volume",
x = "EDV",
y = "Count") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
Visualizing the number of facilities per state
# Data preparation
facility_counts <- HipKneeClean %>%
group_by(State) %>%
summarise(Count = n(), .groups = 'drop')
# Check the first few rows
head(facility_counts)## # A tibble: 6 × 2
## State Count
## <chr> <int>
## 1 AK 21
## 2 AL 88
## 3 AR 79
## 4 AS 1
## 5 AZ 82
## 6 CA 327# Get state boundaries
states_map <- map_data("state")
# Create a mapping from state abbreviations to full state names
state_mapping <- data.frame(
State = state.abb,
full_state_name = tolower(state.name),
stringsAsFactors = FALSE
)
# Add full state names to facility_counts
facility_counts <- merge(facility_counts, state_mapping, by.x = "State", by.y = "State")
# Join facility counts with state map data
facility_map_data <- left_join(states_map, facility_counts, by = c("region" = "full_state_name"))
# Replace NA values with 0 in the Count column
facility_map_data$Count[is.na(facility_map_data$Count)] <- 0
# Plot the map with facility counts
ggplot(data = facility_map_data) +
geom_polygon(aes(x = long, y = lat, group = group, fill = Count), color = "white") +
scale_fill_gradient(low = "lightblue", high = "darkblue", na.value = "grey50", name = "Facility Count") +
theme_minimal() +
labs(title = "Figure 2. Number of Facilities per State") +
theme(axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.background = element_blank())
Visualizing the average PredictedReadmissionRate_HIP-KNEE per state
# Rename column
HipKneeClean <- HipKneeClean %>%
rename(PredictedReadmissionRate_HIP_KNEE = `PredictedReadmissionRate_HIP-KNEE`)
# Calculate the average PredictedReadmissionRate_HIP-KNEE per state
average_readmission_rate <- HipKneeClean %>%
group_by(State) %>%
summarize(Average_PredictedReadmissionRate_HIP_KNEE = mean(PredictedReadmissionRate_HIP_KNEE, na.rm = TRUE))
# Add full state names to the average readmission rate data
average_readmission_rate <- merge(average_readmission_rate, state_mapping, by.x = "State", by.y = "State")
# Join average readmission rate with state map data
readmission_map_data <- left_join(states_map, average_readmission_rate, by = c("region" = "full_state_name"))
# Plot the map with average readmission rates
ggplot(data = readmission_map_data) +
geom_polygon(aes(x = long, y = lat, group = group, fill = Average_PredictedReadmissionRate_HIP_KNEE), color = "white") +
scale_fill_gradient(low = "lightgreen", high = "darkgreen", name = "Average Predicted\nReadmission Rate") +
theme_minimal() +
labs(title = "Figure 3. Average Predicted Readmission Rate for Hip/Knee Replacement per State") +
theme(axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.background = element_blank())
Visualizing the spread of the target variable (PredictedReadmissionRate_HIP_KNEE)
# Create a histogram of PredictedReadmissionRate_HIP_KNEE
ggplot(HipKneeClean, aes(x = PredictedReadmissionRate_HIP_KNEE)) +
geom_histogram(binwidth = 0.25, fill = "skyblue", color = "black") +
labs(title = "Figure 4. Histogram of Predicted Readmission Rate for Hip/Knee Replacement",
x = "Predicted Readmission Rate",
y = "Frequency") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())## Warning: Removed 2978 rows containing non-finite values (`stat_bin()`).
Creating a table of missing values
# Calculate missing values
missing_values_summary <- HipKneeClean %>%
summarise(across(everything(), ~ sum(is.na(.)))) %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Missing_Count") %>%
mutate(Missing_Percentage = (Missing_Count / nrow(HipKneeClean)) * 100)
# Print the table using kable
missing_values_summary %>%
kable(caption = "Table 7. Missing Values Summary") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | Missing_Count | Missing_Percentage |
|---|---|---|
| FacilityId | 0 | 0.000000 |
| ExcessReadmissionRatio_HIP-KNEE | 2978 | 61.835548 |
| PredictedReadmissionRate_HIP_KNEE | 2978 | 61.835548 |
| ExpectedReadmissionRate_HIP-KNEE | 2978 | 61.835548 |
| NumberOfReadmissions_HIP-KNEE | 2978 | 61.835548 |
| PatientSurveyStarRating_H_COMP_1_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_2_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_3_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_5_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_6_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_7_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_CLEAN_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_QUIET_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_RECMND_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_STAR_RATING | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | 1561 | 32.412791 |
| EDV | 972 | 20.182724 |
| ED_2_Strata_1 | 3709 | 77.014120 |
| HCP_COVID_19 | 1183 | 24.563954 |
| IMM_3 | 676 | 14.036545 |
| OP_18b | 749 | 15.552326 |
| OP_18c | 1718 | 35.672758 |
| OP_22 | 975 | 20.245017 |
| OP_23 | 3281 | 68.127076 |
| OP_29 | 1986 | 41.237541 |
| SAFE_USE_OF_OPIOIDS | 1146 | 23.795681 |
| SEP_1 | 1719 | 35.693522 |
| SEP_SH_3HR | 2196 | 45.598007 |
| SEP_SH_6HR | 2777 | 57.661960 |
| SEV_SEP_3HR | 1730 | 35.921927 |
| SEV_SEP_6HR | 1879 | 39.015781 |
| STK_02 | 3279 | 68.085548 |
| STK_05 | 3722 | 77.284053 |
| STK_06 | 3518 | 73.048173 |
| VTE_1 | 2600 | 53.986711 |
| VTE_2 | 3403 | 70.660299 |
| Score_COMP_HIP_KNEE | 2726 | 56.602990 |
| Score_MORT_30_AMI | 2873 | 59.655316 |
| Score_MORT_30_COPD | 2247 | 46.656977 |
| Score_MORT_30_HF | 1760 | 36.544851 |
| Score_MORT_30_PN | 1302 | 27.034884 |
| Score_MORT_30_STK | 2693 | 55.917774 |
| Score_PSI_03 | 1647 | 34.198505 |
| Score_PSI_04 | 3207 | 66.590532 |
| Score_PSI_06 | 1628 | 33.803987 |
| Score_PSI_08 | 1627 | 33.783223 |
| Score_PSI_09 | 1886 | 39.161130 |
| Score_PSI_10 | 2223 | 46.158638 |
| Score_PSI_11 | 2213 | 45.950997 |
| Score_PSI_12 | 1881 | 39.057309 |
| Score_PSI_13 | 2267 | 47.072259 |
| Score_PSI_14 | 2224 | 46.179402 |
| Score_PSI_15 | 1900 | 39.451827 |
| Score_PSI_90 | 1805 | 37.479236 |
| FacilityName | 171 | 3.550664 |
| State | 171 | 3.550664 |
| Payment_PAYM_90_HIP_KNEE | 2815 | 58.450997 |
Assessing collinearity
# Compute correlation matrix
cor_matrix <- cor(HipKneeClean %>% select_if(is.numeric), use = "pairwise.complete.obs")
# Melt the correlation matrix into a long format
cor_melted <- melt(cor_matrix)
# Plot the heatmap
ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), name = "Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Figure 5. Correlation Heatmap of Numeric Variables")
# Convert the correlation matrix to a data frame
cor_table <- as.data.frame(cor_matrix)
# Add variable names as a column for better readability
cor_table$Variable <- rownames(cor_table)
# Reorder columns for better readability
cor_table <- cor_table %>%
select(Variable, everything())
# Print the table using kable
cor_table %>%
kable(caption = "Table 8. Correlation Coefficients Table") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | ExcessReadmissionRatio_HIP-KNEE | PredictedReadmissionRate_HIP_KNEE | ExpectedReadmissionRate_HIP-KNEE | NumberOfReadmissions_HIP-KNEE | PatientSurveyStarRating_H_COMP_1_STAR_RATING | PatientSurveyStarRating_H_COMP_2_STAR_RATING | PatientSurveyStarRating_H_COMP_3_STAR_RATING | PatientSurveyStarRating_H_COMP_5_STAR_RATING | PatientSurveyStarRating_H_COMP_6_STAR_RATING | PatientSurveyStarRating_H_COMP_7_STAR_RATING | PatientSurveyStarRating_H_CLEAN_STAR_RATING | PatientSurveyStarRating_H_QUIET_STAR_RATING | PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | PatientSurveyStarRating_H_RECMND_STAR_RATING | PatientSurveyStarRating_H_STAR_RATING | HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | ED_2_Strata_1 | HCP_COVID_19 | IMM_3 | OP_18b | OP_18c | OP_22 | OP_23 | OP_29 | SAFE_USE_OF_OPIOIDS | SEP_1 | SEP_SH_3HR | SEP_SH_6HR | SEV_SEP_3HR | SEV_SEP_6HR | STK_02 | STK_05 | STK_06 | VTE_1 | VTE_2 | Score_COMP_HIP_KNEE | Score_MORT_30_AMI | Score_MORT_30_COPD | Score_MORT_30_HF | Score_MORT_30_PN | Score_MORT_30_STK | Score_PSI_03 | Score_PSI_04 | Score_PSI_06 | Score_PSI_08 | Score_PSI_09 | Score_PSI_10 | Score_PSI_11 | Score_PSI_12 | Score_PSI_13 | Score_PSI_14 | Score_PSI_15 | Score_PSI_90 | Payment_PAYM_90_HIP_KNEE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ExcessReadmissionRatio_HIP-KNEE | ExcessReadmissionRatio_HIP-KNEE | 1.0000000 | 0.6851738 | 0.0934639 | 0.0201602 | -0.1590972 | -0.1749847 | -0.1597408 | -0.1551935 | -0.1771868 | -0.1941803 | -0.1160735 | -0.1047116 | -0.1709175 | -0.1659007 | -0.1783759 | -0.1666746 | -0.1802262 | -0.1831908 | -0.1775098 | -0.1844376 | -0.1877798 | -0.1223047 | -0.1123794 | -0.1779322 | -0.1852873 | 0.0762327 | -0.0508152 | -0.0583326 | 0.0505288 | 0.0838395 | 0.0252830 | 0.0485500 | -0.0296026 | 0.0627005 | -0.0309171 | -0.0529663 | 0.0313734 | -0.0322822 | -0.0278551 | -0.0434539 | -0.0205857 | -0.0425563 | 0.0039309 | 0.0707936 | 0.4350513 | 0.0392653 | -0.0452304 | -0.0577013 | -0.0046351 | -0.0025521 | -0.0058883 | 0.0129772 | 0.0156305 | 0.0386634 | -0.0459321 | 0.0099239 | 0.1103031 | 0.0939499 | 0.1262199 | -0.0138581 | -0.0019014 | 0.0882354 | 0.2740999 |
| PredictedReadmissionRate_HIP_KNEE | PredictedReadmissionRate_HIP_KNEE | 0.6851738 | 1.0000000 | 0.7840403 | -0.0314679 | -0.2144148 | -0.2287902 | -0.2138329 | -0.2191170 | -0.2029456 | -0.2247172 | -0.1981594 | -0.1748506 | -0.2016141 | -0.1642097 | -0.2308937 | -0.2067264 | -0.2352248 | -0.2449572 | -0.2546502 | -0.2079982 | -0.2250397 | -0.2045779 | -0.1801665 | -0.2060912 | -0.1911254 | 0.1082272 | -0.0563082 | -0.0028840 | 0.1295727 | 0.0877808 | 0.0501727 | 0.0614226 | -0.0106510 | 0.1063002 | -0.0326387 | -0.0689850 | 0.0231777 | -0.0375511 | -0.0014846 | 0.0066866 | -0.0801055 | -0.0101158 | 0.0654668 | 0.1064994 | 0.3208550 | 0.0074065 | -0.0794948 | -0.1067828 | -0.0985660 | -0.0376746 | -0.0037334 | -0.0449077 | 0.0154891 | -0.0214412 | -0.0182303 | 0.0710046 | 0.1130121 | 0.1047402 | 0.1193336 | 0.0140012 | -0.0158282 | 0.0973882 | 0.2975679 |
| ExpectedReadmissionRate_HIP-KNEE | ExpectedReadmissionRate_HIP-KNEE | 0.0934639 | 0.7840403 | 1.0000000 | -0.0711281 | -0.1696704 | -0.1755397 | -0.1644998 | -0.1788949 | -0.1353119 | -0.1533717 | -0.1829249 | -0.1621036 | -0.1408372 | -0.0923845 | -0.1744075 | -0.1524571 | -0.1798454 | -0.1926175 | -0.2093859 | -0.1348924 | -0.1608376 | -0.1848471 | -0.1649195 | -0.1425272 | -0.1150026 | 0.0843876 | -0.0316503 | 0.0435410 | 0.1403298 | 0.0491515 | 0.0510729 | 0.0366382 | 0.0098417 | 0.0946020 | -0.0189720 | -0.0503969 | 0.0048063 | -0.0227213 | 0.0212143 | 0.0415201 | -0.0867129 | 0.0212012 | 0.0866767 | 0.0781368 | 0.0742579 | -0.0287661 | -0.0683850 | -0.0967183 | -0.1319780 | -0.0478311 | 0.0027232 | -0.0749480 | 0.0017920 | -0.0595834 | 0.0136214 | 0.0894074 | 0.0639191 | 0.0654692 | 0.0603447 | 0.0328153 | -0.0242059 | 0.0626347 | 0.1808580 |
| NumberOfReadmissions_HIP-KNEE | NumberOfReadmissions_HIP-KNEE | 0.0201602 | -0.0314679 | -0.0711281 | 1.0000000 | 0.0810868 | 0.0840057 | 0.0335037 | 0.0564065 | 0.0814452 | 0.1205532 | 0.0328567 | 0.0224509 | 0.1377179 | 0.1564882 | 0.0917032 | 0.0751902 | 0.0840409 | 0.0372919 | 0.0642775 | 0.0782242 | 0.1258866 | 0.0384260 | 0.0313956 | 0.1283278 | 0.1667722 | 0.1093961 | 0.0547945 | 0.0247665 | 0.1042992 | 0.0719260 | 0.0494440 | -0.0595500 | -0.0190168 | 0.0791881 | -0.0049391 | -0.0201727 | 0.0484079 | -0.0083329 | 0.0002631 | 0.0242322 | -0.0093595 | 0.0377761 | 0.0503681 | 0.0677007 | -0.1459884 | -0.1664294 | -0.0875397 | -0.1319899 | -0.1252656 | -0.1373235 | -0.0376057 | -0.0681506 | -0.0571022 | -0.1292021 | -0.0138771 | -0.0604643 | -0.0502829 | -0.0445391 | -0.0694840 | -0.0737703 | -0.0514689 | -0.0836880 | -0.1282785 |
| PatientSurveyStarRating_H_COMP_1_STAR_RATING | PatientSurveyStarRating_H_COMP_1_STAR_RATING | -0.1590972 | -0.2144148 | -0.1696704 | 0.0810868 | 1.0000000 | 0.7652622 | 0.8092146 | 0.7817407 | 0.6947694 | 0.8094552 | 0.5857397 | 0.6176477 | 0.8113195 | 0.7410800 | 0.8821074 | 0.9413741 | 0.7947199 | 0.8439662 | 0.8192660 | 0.7212981 | 0.8279021 | 0.6062342 | 0.6396807 | 0.8361084 | 0.7761632 | -0.3811431 | -0.0217341 | 0.1991033 | -0.3200784 | -0.1816118 | -0.2152685 | 0.0440556 | 0.0700383 | 0.1401327 | 0.1288469 | 0.0438649 | -0.0683424 | 0.1642083 | 0.1503950 | 0.2176041 | 0.1753654 | 0.1870897 | -0.0859359 | -0.0029321 | -0.0761301 | -0.0479940 | -0.0124762 | 0.1078488 | 0.0183801 | -0.0132887 | -0.0219430 | -0.0320167 | -0.0184090 | 0.0046992 | 0.0835767 | -0.0402520 | -0.1459187 | -0.0669148 | -0.1424419 | 0.0017770 | 0.0329127 | -0.1122411 | -0.2121841 |
| PatientSurveyStarRating_H_COMP_2_STAR_RATING | PatientSurveyStarRating_H_COMP_2_STAR_RATING | -0.1749847 | -0.2287902 | -0.1755397 | 0.0840057 | 0.7652622 | 1.0000000 | 0.6820394 | 0.7310740 | 0.6334942 | 0.7745158 | 0.4946512 | 0.5958859 | 0.7576397 | 0.7012869 | 0.8135436 | 0.7932584 | 0.9499147 | 0.7139678 | 0.7675074 | 0.6648163 | 0.8027744 | 0.5120334 | 0.6187305 | 0.7826816 | 0.7365285 | -0.2976284 | 0.0206259 | 0.2149395 | -0.2473453 | -0.1452767 | -0.1462753 | 0.0003840 | 0.0721589 | 0.0759631 | 0.0920878 | 0.0418658 | -0.0697479 | 0.1295914 | 0.0899079 | 0.1940882 | 0.1763581 | 0.1579604 | -0.0887946 | -0.0231504 | -0.0615070 | -0.0650654 | -0.0163367 | 0.0722015 | -0.0021513 | -0.0165143 | -0.0016914 | -0.0041080 | 0.0301520 | -0.0171646 | 0.0956403 | -0.0267891 | -0.1367919 | -0.0390690 | -0.1430591 | -0.0079719 | 0.0359419 | -0.0871039 | -0.1893123 |
| PatientSurveyStarRating_H_COMP_3_STAR_RATING | PatientSurveyStarRating_H_COMP_3_STAR_RATING | -0.1597408 | -0.2138329 | -0.1644998 | 0.0335037 | 0.8092146 | 0.6820394 | 1.0000000 | 0.7583026 | 0.6569534 | 0.7355634 | 0.5956464 | 0.6138696 | 0.7478412 | 0.6650712 | 0.8260329 | 0.8314522 | 0.7052054 | 0.9423558 | 0.7878299 | 0.6812130 | 0.7546488 | 0.6155860 | 0.6319284 | 0.7774308 | 0.6961148 | -0.3896187 | -0.0747053 | 0.1498815 | -0.3981251 | -0.2241446 | -0.2360570 | 0.0486530 | 0.0507759 | 0.0900327 | 0.1321630 | 0.0521192 | -0.0725481 | 0.1605381 | 0.1368127 | 0.1201354 | 0.1413712 | 0.0905514 | -0.0996751 | -0.0477865 | -0.0405313 | -0.0177776 | 0.0302290 | 0.1568119 | 0.0384967 | 0.0435642 | -0.0192053 | -0.0016542 | 0.0274588 | 0.0159553 | 0.0859168 | -0.0262995 | -0.1292050 | -0.0865925 | -0.1330731 | 0.0092188 | 0.0480143 | -0.1032756 | -0.1564699 |
| PatientSurveyStarRating_H_COMP_5_STAR_RATING | PatientSurveyStarRating_H_COMP_5_STAR_RATING | -0.1551935 | -0.2191170 | -0.1788949 | 0.0564065 | 0.7817407 | 0.7310740 | 0.7583026 | 1.0000000 | 0.6659220 | 0.7694318 | 0.5769327 | 0.5922228 | 0.7502954 | 0.6685334 | 0.8320006 | 0.8038535 | 0.7587947 | 0.7931167 | 0.9410632 | 0.6945471 | 0.7903879 | 0.5987567 | 0.6129310 | 0.7793734 | 0.7066986 | -0.3575914 | -0.0062821 | 0.1693415 | -0.3234673 | -0.1918556 | -0.1939137 | 0.0438526 | 0.0664748 | 0.0784938 | 0.1324347 | 0.0587805 | -0.0562912 | 0.1606626 | 0.1423359 | 0.1358181 | 0.1964279 | 0.1045092 | -0.0999738 | -0.0151359 | -0.0450744 | -0.0510480 | -0.0130589 | 0.0644025 | -0.0046993 | 0.0257903 | -0.0221085 | -0.0185639 | 0.0273930 | -0.0011887 | 0.0897240 | -0.0322016 | -0.1375039 | -0.0537461 | -0.1257130 | -0.0064032 | 0.0443475 | -0.1023631 | -0.1576660 |
| PatientSurveyStarRating_H_COMP_6_STAR_RATING | PatientSurveyStarRating_H_COMP_6_STAR_RATING | -0.1771868 | -0.2029456 | -0.1353119 | 0.0814452 | 0.6947694 | 0.6334942 | 0.6569534 | 0.6659220 | 1.0000000 | 0.7186423 | 0.4784757 | 0.4292218 | 0.6769019 | 0.6409371 | 0.7586740 | 0.7386381 | 0.6584544 | 0.6847212 | 0.7015421 | 0.9400388 | 0.7549683 | 0.4999730 | 0.4567781 | 0.7098506 | 0.6770439 | -0.3008610 | 0.0354071 | 0.2404893 | -0.2071783 | -0.1351116 | -0.1225568 | 0.0625152 | 0.1045421 | 0.1184473 | 0.1443955 | 0.0557260 | -0.0637431 | 0.1782560 | 0.1758781 | 0.2091327 | 0.2034132 | 0.1930126 | 0.0235381 | 0.0288319 | -0.0990420 | -0.0682603 | 0.0034724 | 0.1194712 | -0.0343594 | 0.0159051 | 0.0012755 | 0.0047573 | 0.0312923 | -0.0120380 | 0.0883277 | -0.0190535 | -0.1484276 | -0.0612852 | -0.1424055 | -0.0023314 | 0.0662158 | -0.0911960 | -0.2089917 |
| PatientSurveyStarRating_H_COMP_7_STAR_RATING | PatientSurveyStarRating_H_COMP_7_STAR_RATING | -0.1941803 | -0.2247172 | -0.1533717 | 0.1205532 | 0.8094552 | 0.7745158 | 0.7355634 | 0.7694318 | 0.7186423 | 1.0000000 | 0.5720636 | 0.6101555 | 0.8272215 | 0.7939928 | 0.8743494 | 0.8277780 | 0.7995932 | 0.7605870 | 0.8011319 | 0.7433629 | 0.9482189 | 0.5929605 | 0.6371231 | 0.8571093 | 0.8310741 | -0.3553994 | 0.0397427 | 0.2374707 | -0.2572208 | -0.1626711 | -0.2149719 | 0.0359131 | 0.0858822 | 0.1201701 | 0.1393844 | 0.0474753 | -0.0250525 | 0.1732976 | 0.1422983 | 0.2157600 | 0.1380616 | 0.1802407 | -0.0364693 | 0.0393084 | -0.1067242 | -0.1098730 | -0.0673445 | 0.0151872 | -0.0880905 | -0.0653713 | -0.0300329 | -0.0817158 | -0.0009316 | -0.0348072 | 0.0827573 | -0.0399586 | -0.1668264 | -0.0670446 | -0.1593474 | -0.0164980 | 0.0357574 | -0.1311288 | -0.1977109 |
| PatientSurveyStarRating_H_CLEAN_STAR_RATING | PatientSurveyStarRating_H_CLEAN_STAR_RATING | -0.1160735 | -0.1981594 | -0.1829249 | 0.0328567 | 0.5857397 | 0.4946512 | 0.5956464 | 0.5769327 | 0.4784757 | 0.5720636 | 1.0000000 | 0.4987457 | 0.5965227 | 0.5237951 | 0.6391671 | 0.5928460 | 0.5111389 | 0.6220272 | 0.5982789 | 0.4927072 | 0.5781200 | 0.9570846 | 0.5105668 | 0.6248221 | 0.5505967 | -0.3195008 | -0.0225176 | 0.0814169 | -0.3267508 | -0.1804160 | -0.2479612 | 0.0190913 | 0.0137026 | 0.0598688 | 0.1690161 | 0.0858056 | -0.0192334 | 0.1869822 | 0.1376097 | 0.0367533 | 0.0909053 | 0.0153703 | -0.0848392 | -0.0027352 | -0.0571772 | -0.0630079 | -0.0352455 | 0.0665845 | 0.0009844 | -0.0651509 | -0.0629368 | -0.1272277 | -0.0390292 | 0.0016051 | -0.0069280 | -0.0851994 | -0.1286917 | -0.0807406 | -0.1231640 | -0.0459005 | -0.0007875 | -0.1463724 | -0.0409475 |
| PatientSurveyStarRating_H_QUIET_STAR_RATING | PatientSurveyStarRating_H_QUIET_STAR_RATING | -0.1047116 | -0.1748506 | -0.1621036 | 0.0224509 | 0.6176477 | 0.5958859 | 0.6138696 | 0.5922228 | 0.4292218 | 0.6101555 | 0.4987457 | 1.0000000 | 0.6313199 | 0.5470896 | 0.6730863 | 0.6317832 | 0.6199984 | 0.6414418 | 0.6171349 | 0.4395790 | 0.6249789 | 0.5123475 | 0.9556614 | 0.6537481 | 0.5755335 | -0.3375393 | -0.1460561 | 0.0866767 | -0.3615058 | -0.1742767 | -0.2095914 | 0.0018524 | -0.0044671 | 0.0303764 | 0.0919836 | 0.0246699 | -0.0291912 | 0.1003528 | 0.1010108 | 0.0765341 | 0.1069590 | 0.0440800 | -0.0897193 | -0.0754020 | -0.0250746 | 0.0428372 | 0.0722857 | 0.1510853 | 0.0902457 | 0.0337813 | -0.0457618 | -0.0343542 | -0.0062045 | 0.0093687 | 0.0220979 | -0.0051357 | -0.0772394 | -0.0730231 | -0.1033157 | -0.0277664 | -0.0213211 | -0.0977954 | -0.0612456 |
| PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | -0.1709175 | -0.2016141 | -0.1408372 | 0.1377179 | 0.8113195 | 0.7576397 | 0.7478412 | 0.7502954 | 0.6769019 | 0.8272215 | 0.5965227 | 0.6313199 | 1.0000000 | 0.8595636 | 0.8714961 | 0.8454781 | 0.7811879 | 0.7821354 | 0.7928741 | 0.7081668 | 0.8548308 | 0.6252851 | 0.6598602 | 0.9428150 | 0.9030319 | -0.3410053 | 0.0325646 | 0.2088800 | -0.2358015 | -0.1677838 | -0.2095003 | 0.0163640 | 0.0773879 | 0.1105834 | 0.1692958 | 0.0877507 | 0.0093791 | 0.1897215 | 0.1544748 | 0.2160677 | 0.1589966 | 0.2063053 | -0.0348377 | 0.0691782 | -0.0967251 | -0.0951559 | -0.0338034 | 0.0240132 | -0.0613852 | -0.0814552 | -0.0490180 | -0.0852573 | -0.0166476 | -0.0686735 | 0.0710883 | -0.0270735 | -0.1585705 | -0.0643414 | -0.1472927 | -0.0205437 | 0.0287970 | -0.1387904 | -0.2018848 |
| PatientSurveyStarRating_H_RECMND_STAR_RATING | PatientSurveyStarRating_H_RECMND_STAR_RATING | -0.1659007 | -0.1642097 | -0.0923845 | 0.1564882 | 0.7410800 | 0.7012869 | 0.6650712 | 0.6685334 | 0.6409371 | 0.7939928 | 0.5237951 | 0.5470896 | 0.8595636 | 1.0000000 | 0.7960527 | 0.7806696 | 0.7300827 | 0.6882451 | 0.7037121 | 0.6761773 | 0.8247493 | 0.5530915 | 0.5738169 | 0.9052988 | 0.9480759 | -0.2979673 | 0.0850940 | 0.2212547 | -0.1390380 | -0.1405352 | -0.1692993 | -0.0078546 | 0.0885267 | 0.1190673 | 0.1532363 | 0.0735792 | 0.0265116 | 0.1760486 | 0.1358664 | 0.2348521 | 0.1378362 | 0.2020601 | 0.0037855 | 0.0981665 | -0.1246042 | -0.0981817 | -0.0266626 | -0.0005694 | -0.1107731 | -0.1033645 | -0.0387585 | -0.0857042 | -0.0221487 | -0.0800058 | 0.0787681 | 0.0075443 | -0.1576927 | -0.0393015 | -0.1297623 | 0.0134380 | 0.0262619 | -0.1202753 | -0.2235957 |
| PatientSurveyStarRating_H_STAR_RATING | PatientSurveyStarRating_H_STAR_RATING | -0.1783759 | -0.2308937 | -0.1744075 | 0.0917032 | 0.8821074 | 0.8135436 | 0.8260329 | 0.8320006 | 0.7586740 | 0.8743494 | 0.6391671 | 0.6730863 | 0.8714961 | 0.7960527 | 1.0000000 | 0.8920184 | 0.8314222 | 0.8501602 | 0.8592370 | 0.7769593 | 0.8837377 | 0.6599688 | 0.6959526 | 0.8852220 | 0.8308877 | -0.3910783 | 0.0049679 | 0.2156832 | -0.3210235 | -0.2011786 | -0.2229136 | 0.0373120 | 0.0727680 | 0.1107188 | 0.1500361 | 0.0637937 | -0.0494590 | 0.1800467 | 0.1591593 | 0.1860190 | 0.1945060 | 0.1646616 | -0.0552836 | 0.0051809 | -0.0822439 | -0.0710775 | -0.0009117 | 0.0947303 | -0.0145406 | -0.0275058 | -0.0283146 | -0.0547617 | 0.0061083 | -0.0156145 | 0.0913137 | -0.0452172 | -0.1540619 | -0.0738343 | -0.1614933 | -0.0003415 | 0.0303266 | -0.1263561 | -0.1981220 |
| HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | -0.1666746 | -0.2067264 | -0.1524571 | 0.0751902 | 0.9413741 | 0.7932584 | 0.8314522 | 0.8038535 | 0.7386381 | 0.8277780 | 0.5928460 | 0.6317832 | 0.8454781 | 0.7806696 | 0.8920184 | 1.0000000 | 0.8343982 | 0.8821498 | 0.8499295 | 0.7853207 | 0.8750498 | 0.6213823 | 0.6671546 | 0.8901557 | 0.8322887 | -0.3639238 | -0.0217481 | 0.2169674 | -0.3107282 | -0.1745392 | -0.2104045 | 0.0422069 | 0.0800014 | 0.1588429 | 0.1603505 | 0.0495956 | -0.0522724 | 0.1983566 | 0.1847906 | 0.2367758 | 0.2012691 | 0.2088467 | -0.0544304 | 0.0173084 | -0.0796721 | -0.0452192 | -0.0003548 | 0.1228267 | 0.0156651 | -0.0170135 | -0.0236955 | -0.0303324 | -0.0019546 | -0.0080622 | 0.0845264 | -0.0270956 | -0.1428877 | -0.0663743 | -0.1459851 | -0.0029199 | 0.0385911 | -0.1096342 | -0.2107792 |
| HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | -0.1802262 | -0.2352248 | -0.1798454 | 0.0840409 | 0.7947199 | 0.9499147 | 0.7052054 | 0.7587947 | 0.6584544 | 0.7995932 | 0.5111389 | 0.6199984 | 0.7811879 | 0.7300827 | 0.8314222 | 0.8343982 | 1.0000000 | 0.7459134 | 0.8003061 | 0.6977774 | 0.8394911 | 0.5323986 | 0.6495609 | 0.8179081 | 0.7709408 | -0.3129324 | 0.0155923 | 0.2128270 | -0.2565866 | -0.1409829 | -0.1463552 | 0.0002792 | 0.0782762 | 0.0802578 | 0.1011092 | 0.0415646 | -0.0658150 | 0.1433814 | 0.0975333 | 0.1984598 | 0.1920243 | 0.1718119 | -0.0972506 | -0.0314520 | -0.0680571 | -0.0565823 | -0.0027657 | 0.0845920 | 0.0121242 | 0.0058744 | 0.0019106 | -0.0063422 | 0.0334780 | -0.0028885 | 0.0956884 | -0.0236698 | -0.1414366 | -0.0380979 | -0.1394736 | -0.0143285 | 0.0425664 | -0.0826332 | -0.1875503 |
| HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | -0.1831908 | -0.2449572 | -0.1926175 | 0.0372919 | 0.8439662 | 0.7139678 | 0.9423558 | 0.7931167 | 0.6847212 | 0.7605870 | 0.6220272 | 0.6414418 | 0.7821354 | 0.6882451 | 0.8501602 | 0.8821498 | 0.7459134 | 1.0000000 | 0.8363904 | 0.7154682 | 0.7981273 | 0.6483447 | 0.6695835 | 0.8221836 | 0.7369761 | -0.3677548 | -0.0905698 | 0.1363775 | -0.4107053 | -0.2259603 | -0.2585483 | 0.0412981 | 0.0395669 | 0.0998162 | 0.1511649 | 0.0576566 | -0.0681526 | 0.1772872 | 0.1643799 | 0.1340445 | 0.1572709 | 0.1065507 | -0.1148325 | -0.0405641 | -0.0605862 | -0.0215130 | 0.0433887 | 0.1663036 | 0.0511012 | 0.0476323 | -0.0223315 | -0.0117228 | 0.0152153 | 0.0187552 | 0.0790559 | -0.0330533 | -0.1408040 | -0.0953948 | -0.1388091 | -0.0063199 | 0.0405341 | -0.1131291 | -0.1799323 |
| HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | -0.1775098 | -0.2546502 | -0.2093859 | 0.0642775 | 0.8192660 | 0.7675074 | 0.7878299 | 0.9410632 | 0.7015421 | 0.8011319 | 0.5982789 | 0.6171349 | 0.7928741 | 0.7037121 | 0.8592370 | 0.8499295 | 0.8003061 | 0.8363904 | 1.0000000 | 0.7405838 | 0.8369260 | 0.6236211 | 0.6436063 | 0.8239488 | 0.7494949 | -0.3259347 | -0.0050286 | 0.1728899 | -0.3312654 | -0.1928398 | -0.1986827 | 0.0259892 | 0.0644512 | 0.0816216 | 0.1501584 | 0.0726417 | -0.0510706 | 0.1823598 | 0.1625916 | 0.1647114 | 0.2084062 | 0.1450104 | -0.1112139 | -0.0255467 | -0.0557335 | -0.0641464 | -0.0091794 | 0.0704411 | -0.0036037 | 0.0323273 | -0.0128473 | -0.0052418 | 0.0247052 | -0.0039459 | 0.0793646 | -0.0452983 | -0.1557561 | -0.0685223 | -0.1386420 | -0.0063967 | 0.0420468 | -0.1086247 | -0.1660056 |
| HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | -0.1844376 | -0.2079982 | -0.1348924 | 0.0782242 | 0.7212981 | 0.6648163 | 0.6812130 | 0.6945471 | 0.9400388 | 0.7433629 | 0.4927072 | 0.4395790 | 0.7081668 | 0.6761773 | 0.7769593 | 0.7853207 | 0.6977774 | 0.7154682 | 0.7405838 | 1.0000000 | 0.7968806 | 0.5182906 | 0.4744589 | 0.7556492 | 0.7238004 | -0.2900150 | 0.0369924 | 0.2564050 | -0.2066316 | -0.1376094 | -0.1292560 | 0.0701172 | 0.1220570 | 0.1274513 | 0.1720468 | 0.0650279 | -0.0549999 | 0.2122738 | 0.2084118 | 0.2236306 | 0.2431733 | 0.2738285 | 0.0383041 | 0.0559072 | -0.1078990 | -0.0733482 | 0.0081764 | 0.1281267 | -0.0315360 | 0.0086356 | 0.0049870 | 0.0228481 | 0.0256121 | -0.0176853 | 0.0859133 | -0.0145875 | -0.1539039 | -0.0536226 | -0.1430776 | 0.0069610 | 0.0649921 | -0.0872152 | -0.2310210 |
| HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | -0.1877798 | -0.2250397 | -0.1608376 | 0.1258866 | 0.8279021 | 0.8027744 | 0.7546488 | 0.7903879 | 0.7549683 | 0.9482189 | 0.5781200 | 0.6249789 | 0.8548308 | 0.8247493 | 0.8837377 | 0.8750498 | 0.8394911 | 0.7981273 | 0.8369260 | 0.7968806 | 1.0000000 | 0.6025716 | 0.6612420 | 0.9026621 | 0.8770384 | -0.3383196 | 0.0347599 | 0.2352166 | -0.2492489 | -0.1534787 | -0.2043225 | 0.0287005 | 0.0923400 | 0.1255794 | 0.1636239 | 0.0630912 | -0.0194129 | 0.2014907 | 0.1664733 | 0.2341396 | 0.1543470 | 0.2096414 | -0.0268317 | 0.0534239 | -0.1051209 | -0.1115171 | -0.0514076 | 0.0259241 | -0.0829950 | -0.0564195 | -0.0211932 | -0.0638071 | 0.0098029 | -0.0381375 | 0.0825062 | -0.0316508 | -0.1629600 | -0.0614646 | -0.1636005 | -0.0189605 | 0.0320446 | -0.1202132 | -0.2069970 |
| HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | -0.1223047 | -0.2045779 | -0.1848471 | 0.0384260 | 0.6062342 | 0.5120334 | 0.6155860 | 0.5987567 | 0.4999730 | 0.5929605 | 0.9570846 | 0.5123475 | 0.6252851 | 0.5530915 | 0.6599688 | 0.6213823 | 0.5323986 | 0.6483447 | 0.6236211 | 0.5182906 | 0.6025716 | 1.0000000 | 0.5250997 | 0.6604779 | 0.5844106 | -0.3337655 | -0.0182565 | 0.0860619 | -0.3285392 | -0.1777808 | -0.2595566 | 0.0253586 | 0.0135852 | 0.0584827 | 0.1855312 | 0.1034169 | -0.0099818 | 0.2012726 | 0.1514801 | 0.0455756 | 0.1000540 | 0.0284015 | -0.0880614 | -0.0051760 | -0.0539829 | -0.0624749 | -0.0255442 | 0.0677998 | -0.0009274 | -0.0795298 | -0.0709592 | -0.1182074 | -0.0369162 | 0.0027541 | -0.0109302 | -0.0753999 | -0.1233582 | -0.0813748 | -0.1261494 | -0.0505286 | 0.0061946 | -0.1486299 | -0.0439537 |
| HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | -0.1123794 | -0.1801665 | -0.1649195 | 0.0313956 | 0.6396807 | 0.6187305 | 0.6319284 | 0.6129310 | 0.4567781 | 0.6371231 | 0.5105668 | 0.9556614 | 0.6598602 | 0.5738169 | 0.6959526 | 0.6671546 | 0.6495609 | 0.6695835 | 0.6436063 | 0.4744589 | 0.6612420 | 0.5250997 | 1.0000000 | 0.6896579 | 0.6107149 | -0.3437480 | -0.1442006 | 0.1064024 | -0.3661649 | -0.1699262 | -0.2145544 | -0.0022965 | 0.0082056 | 0.0494650 | 0.0908135 | 0.0158871 | -0.0303120 | 0.1023846 | 0.1022135 | 0.0817038 | 0.1098322 | 0.0437557 | -0.0886019 | -0.0634120 | -0.0341816 | 0.0366437 | 0.0773892 | 0.1553155 | 0.0950135 | 0.0255069 | -0.0454692 | -0.0450913 | -0.0062861 | 0.0051695 | 0.0262747 | -0.0054371 | -0.0789391 | -0.0813685 | -0.1096072 | -0.0256155 | -0.0196124 | -0.1003157 | -0.0653730 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | -0.1779322 | -0.2060912 | -0.1425272 | 0.1283278 | 0.8361084 | 0.7826816 | 0.7774308 | 0.7793734 | 0.7098506 | 0.8571093 | 0.6248221 | 0.6537481 | 0.9428150 | 0.9052988 | 0.8852220 | 0.8901557 | 0.8179081 | 0.8221836 | 0.8239488 | 0.7556492 | 0.9026621 | 0.6604779 | 0.6896579 | 1.0000000 | 0.9580767 | -0.3521196 | 0.0302154 | 0.2182505 | -0.2448842 | -0.1752428 | -0.2290270 | 0.0077229 | 0.0865028 | 0.1100002 | 0.1826272 | 0.0844502 | 0.0072643 | 0.2115650 | 0.1691943 | 0.2211843 | 0.1738457 | 0.2315523 | -0.0248375 | 0.0775236 | -0.1091775 | -0.0952111 | -0.0230632 | 0.0300940 | -0.0702915 | -0.0760295 | -0.0499063 | -0.0948401 | -0.0054742 | -0.0590998 | 0.0707774 | -0.0157615 | -0.1615833 | -0.0674588 | -0.1458284 | -0.0172231 | 0.0304634 | -0.1390570 | -0.2108956 |
| HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | -0.1852873 | -0.1911254 | -0.1150026 | 0.1667722 | 0.7761632 | 0.7365285 | 0.6961148 | 0.7066986 | 0.6770439 | 0.8310741 | 0.5505967 | 0.5755335 | 0.9030319 | 0.9480759 | 0.8308877 | 0.8322887 | 0.7709408 | 0.7369761 | 0.7494949 | 0.7238004 | 0.8770384 | 0.5844106 | 0.6107149 | 0.9580767 | 1.0000000 | -0.2951331 | 0.0831560 | 0.2197432 | -0.1585545 | -0.1471003 | -0.1980854 | -0.0179814 | 0.0967347 | 0.1229267 | 0.1676529 | 0.0687394 | 0.0292287 | 0.1933820 | 0.1501468 | 0.2439658 | 0.1443141 | 0.2442083 | 0.0058311 | 0.1184210 | -0.1332048 | -0.1095716 | -0.0262870 | -0.0003806 | -0.1100101 | -0.1074875 | -0.0363290 | -0.1008569 | -0.0193730 | -0.0826240 | 0.0862428 | -0.0105068 | -0.1710237 | -0.0510987 | -0.1364494 | 0.0003983 | 0.0331751 | -0.1279752 | -0.2364653 |
| ED_2_Strata_1 | ED_2_Strata_1 | 0.0762327 | 0.1082272 | 0.0843876 | 0.1093961 | -0.3811431 | -0.2976284 | -0.3896187 | -0.3575914 | -0.3008610 | -0.3553994 | -0.3195008 | -0.3375393 | -0.3410053 | -0.2979673 | -0.3910783 | -0.3639238 | -0.3129324 | -0.3677548 | -0.3259347 | -0.2900150 | -0.3383196 | -0.3337655 | -0.3437480 | -0.3521196 | -0.2951331 | 1.0000000 | 0.1248128 | 0.0186099 | 0.5775206 | 0.4204676 | 0.3419958 | -0.0684821 | 0.0164159 | -0.0486040 | -0.1152627 | -0.0615764 | -0.0354024 | -0.1265965 | -0.0558505 | -0.0983294 | 0.0254940 | -0.0925059 | 0.0761139 | 0.0321584 | 0.0673396 | 0.0579101 | -0.0537002 | -0.1136600 | -0.0389080 | 0.0005912 | 0.0602674 | -0.0541503 | 0.0551291 | -0.0211509 | -0.0336614 | 0.0315830 | 0.1430203 | 0.1038617 | 0.0817412 | 0.0633107 | -0.0393782 | 0.1286484 | 0.1212071 |
| HCP_COVID_19 | HCP_COVID_19 | -0.0508152 | -0.0563082 | -0.0316503 | 0.0547945 | -0.0217341 | 0.0206259 | -0.0747053 | -0.0062821 | 0.0354071 | 0.0397427 | -0.0225176 | -0.1460561 | 0.0325646 | 0.0850940 | 0.0049679 | -0.0217481 | 0.0155923 | -0.0905698 | -0.0050286 | 0.0369924 | 0.0347599 | -0.0182565 | -0.1442006 | 0.0302154 | 0.0831560 | 0.1248128 | 1.0000000 | 0.3203622 | 0.2574291 | 0.0698819 | 0.1122982 | -0.0306155 | 0.1067941 | -0.0812735 | -0.0345104 | 0.0310392 | -0.0124470 | -0.0175650 | -0.1149175 | 0.0947908 | 0.0304334 | 0.0831624 | 0.0241622 | -0.0151698 | -0.0510683 | -0.0869890 | -0.1128278 | -0.1245435 | -0.1523779 | -0.0988833 | 0.0943953 | 0.0417007 | 0.0225916 | -0.0272232 | 0.0549990 | 0.0009222 | -0.0909811 | 0.1091949 | -0.0160408 | 0.0169512 | 0.0430812 | 0.0534106 | -0.0627505 |
| IMM_3 | IMM_3 | -0.0583326 | -0.0028840 | 0.0435410 | 0.0247665 | 0.1991033 | 0.2149395 | 0.1498815 | 0.1693415 | 0.2404893 | 0.2374707 | 0.0814169 | 0.0866767 | 0.2088800 | 0.2212547 | 0.2156832 | 0.2169674 | 0.2128270 | 0.1363775 | 0.1728899 | 0.2564050 | 0.2352166 | 0.0860619 | 0.1064024 | 0.2182505 | 0.2197432 | 0.0186099 | 0.3203622 | 1.0000000 | 0.1105628 | 0.0343661 | 0.0560372 | 0.0400235 | 0.1317922 | 0.0410289 | 0.0439297 | 0.0519538 | -0.0201631 | 0.0361783 | 0.0484998 | 0.1318005 | 0.1020708 | 0.0900367 | 0.0906329 | 0.0058144 | -0.0212916 | -0.0165321 | -0.0616051 | -0.0010634 | -0.0761104 | 0.0146397 | 0.0451508 | 0.0601831 | 0.0544576 | -0.0311579 | 0.0899361 | 0.0412713 | -0.0625676 | 0.0594933 | -0.0250714 | 0.0723872 | 0.0625753 | 0.0226639 | -0.0720431 |
| OP_18b | OP_18b | 0.0505288 | 0.1295727 | 0.1403298 | 0.1042992 | -0.3200784 | -0.2473453 | -0.3981251 | -0.3234673 | -0.2071783 | -0.2572208 | -0.3267508 | -0.3615058 | -0.2358015 | -0.1390380 | -0.3210235 | -0.3107282 | -0.2565866 | -0.4107053 | -0.3312654 | -0.2066316 | -0.2492489 | -0.3285392 | -0.3661649 | -0.2448842 | -0.1585545 | 0.5775206 | 0.2574291 | 0.1105628 | 1.0000000 | 0.4959758 | 0.5894838 | -0.0756249 | 0.0506067 | -0.1400845 | -0.1714513 | -0.0557159 | 0.0160417 | -0.2007893 | -0.1593730 | 0.0723310 | -0.1190028 | 0.0948584 | 0.2344307 | 0.0629824 | -0.0293698 | -0.0678837 | -0.1527290 | -0.2195933 | -0.1858291 | -0.0905644 | 0.0583644 | 0.0638412 | 0.0187794 | -0.0806516 | 0.0224833 | 0.0544528 | -0.0076797 | 0.1653812 | 0.0993570 | 0.0676972 | 0.0374530 | 0.0986077 | -0.0241965 |
| OP_18c | OP_18c | 0.0838395 | 0.0877808 | 0.0491515 | 0.0719260 | -0.1816118 | -0.1452767 | -0.2241446 | -0.1918556 | -0.1351116 | -0.1626711 | -0.1804160 | -0.1742767 | -0.1677838 | -0.1405352 | -0.2011786 | -0.1745392 | -0.1409829 | -0.2259603 | -0.1928398 | -0.1376094 | -0.1534787 | -0.1777808 | -0.1699262 | -0.1752428 | -0.1471003 | 0.4204676 | 0.0698819 | 0.0343661 | 0.4959758 | 1.0000000 | 0.3393524 | 0.0090382 | 0.0430774 | -0.0573485 | -0.0726374 | -0.0430698 | 0.0018369 | -0.0844129 | -0.0481731 | -0.0076033 | -0.0476864 | 0.0158359 | 0.1235925 | 0.0408574 | 0.0028501 | -0.0296718 | -0.0977614 | -0.1375348 | -0.0578145 | -0.0463395 | 0.0181970 | 0.0046272 | 0.0392571 | -0.0457654 | -0.0128057 | 0.0031479 | 0.0074122 | 0.0486693 | 0.0559662 | 0.0192234 | 0.0049833 | 0.0401196 | 0.0264108 |
| OP_22 | OP_22 | 0.0252830 | 0.0501727 | 0.0510729 | 0.0494440 | -0.2152685 | -0.1462753 | -0.2360570 | -0.1939137 | -0.1225568 | -0.2149719 | -0.2479612 | -0.2095914 | -0.2095003 | -0.1692993 | -0.2229136 | -0.2104045 | -0.1463552 | -0.2585483 | -0.1986827 | -0.1292560 | -0.2043225 | -0.2595566 | -0.2145544 | -0.2290270 | -0.1980854 | 0.3419958 | 0.1122982 | 0.0560372 | 0.5894838 | 0.3393524 | 1.0000000 | -0.0949210 | 0.0293870 | -0.1014242 | -0.2178259 | -0.1066885 | -0.0990718 | -0.2279495 | -0.1444805 | -0.0066518 | -0.0291353 | 0.0057888 | 0.0805708 | -0.0317460 | 0.0214909 | 0.0376811 | -0.0446228 | -0.0767863 | -0.0559254 | 0.0198257 | 0.0648778 | 0.1019307 | 0.0452860 | 0.0102293 | 0.0426036 | 0.0343100 | 0.0397075 | 0.0778917 | 0.0451515 | 0.0652307 | 0.0094012 | 0.0926476 | -0.0237710 |
| OP_23 | OP_23 | 0.0485500 | 0.0614226 | 0.0366382 | -0.0595500 | 0.0440556 | 0.0003840 | 0.0486530 | 0.0438526 | 0.0625152 | 0.0359131 | 0.0190913 | 0.0018524 | 0.0163640 | -0.0078546 | 0.0373120 | 0.0422069 | 0.0002792 | 0.0412981 | 0.0259892 | 0.0701172 | 0.0287005 | 0.0253586 | -0.0022965 | 0.0077229 | -0.0179814 | -0.0684821 | -0.0306155 | 0.0400235 | -0.0756249 | 0.0090382 | -0.0949210 | 1.0000000 | 0.0732988 | 0.0371036 | 0.1919253 | 0.1198314 | 0.0629739 | 0.1834131 | 0.1440580 | 0.0821585 | 0.1352184 | 0.0401311 | 0.2485615 | 0.1664553 | 0.0398944 | 0.0034495 | 0.0135325 | 0.0045056 | -0.0210473 | -0.0614632 | -0.0570477 | -0.0326986 | -0.0515403 | 0.0269822 | -0.0140788 | -0.0045339 | 0.0279871 | -0.0606628 | -0.0384416 | -0.0593032 | -0.0270588 | -0.0530033 | 0.0545394 |
| OP_29 | OP_29 | -0.0296026 | -0.0106510 | 0.0098417 | -0.0190168 | 0.0700383 | 0.0721589 | 0.0507759 | 0.0664748 | 0.1045421 | 0.0858822 | 0.0137026 | -0.0044671 | 0.0773879 | 0.0885267 | 0.0727680 | 0.0800014 | 0.0782762 | 0.0395669 | 0.0644512 | 0.1220570 | 0.0923400 | 0.0135852 | 0.0082056 | 0.0865028 | 0.0967347 | 0.0164159 | 0.1067941 | 0.1317922 | 0.0506067 | 0.0430774 | 0.0293870 | 0.0732988 | 1.0000000 | -0.0650231 | 0.0952846 | 0.0685334 | 0.0455411 | 0.1111026 | 0.0304354 | 0.0653732 | 0.0263917 | 0.0285800 | 0.1567526 | 0.0271825 | -0.0096464 | -0.0600569 | 0.0081252 | 0.0099705 | -0.0536184 | -0.0251654 | -0.0032584 | 0.0312780 | 0.0059688 | -0.0199006 | 0.0150699 | 0.0331569 | -0.0837910 | -0.0108546 | -0.0087678 | 0.0163779 | 0.0419068 | -0.0312757 | -0.0815209 |
| SAFE_USE_OF_OPIOIDS | SAFE_USE_OF_OPIOIDS | 0.0627005 | 0.1063002 | 0.0946020 | 0.0791881 | 0.1401327 | 0.0759631 | 0.0900327 | 0.0784938 | 0.1184473 | 0.1201701 | 0.0598688 | 0.0303764 | 0.1105834 | 0.1190673 | 0.1107188 | 0.1588429 | 0.0802578 | 0.0998162 | 0.0816216 | 0.1274513 | 0.1255794 | 0.0584827 | 0.0494650 | 0.1100002 | 0.1229267 | -0.0486040 | -0.0812735 | 0.0410289 | -0.1400845 | -0.0573485 | -0.1014242 | 0.0371036 | -0.0650231 | 1.0000000 | 0.0650110 | 0.0100193 | 0.0697614 | 0.0854069 | 0.0857646 | 0.1450732 | 0.0858291 | 0.1347354 | -0.0563373 | 0.1913961 | -0.0081923 | -0.0643353 | -0.0362573 | 0.0171605 | -0.0204107 | -0.0804707 | -0.0287158 | -0.0995591 | -0.0603629 | -0.0017558 | -0.0043396 | -0.0382369 | 0.0137283 | -0.0380145 | -0.0258097 | -0.0166146 | -0.0268805 | -0.0300979 | -0.0048449 |
| SEP_1 | SEP_1 | -0.0309171 | -0.0326387 | -0.0189720 | -0.0049391 | 0.1288469 | 0.0920878 | 0.1321630 | 0.1324347 | 0.1443955 | 0.1393844 | 0.1690161 | 0.0919836 | 0.1692958 | 0.1532363 | 0.1500361 | 0.1603505 | 0.1011092 | 0.1511649 | 0.1501584 | 0.1720468 | 0.1636239 | 0.1855312 | 0.0908135 | 0.1826272 | 0.1676529 | -0.1152627 | -0.0345104 | 0.0439297 | -0.1714513 | -0.0726374 | -0.2178259 | 0.1919253 | 0.0952846 | 0.0650110 | 1.0000000 | 0.7309445 | 0.5744973 | 0.8329106 | 0.6460143 | 0.0976908 | 0.0923975 | 0.1245526 | 0.2303117 | 0.2029424 | -0.0347567 | -0.0154393 | 0.0197865 | 0.0170609 | -0.0335808 | -0.0769699 | -0.0797702 | -0.0937443 | -0.0339321 | -0.0179990 | -0.0245784 | -0.0390884 | -0.0564170 | -0.0757690 | -0.0254596 | -0.0644595 | -0.0051105 | -0.1034135 | -0.0078309 |
| SEP_SH_3HR | SEP_SH_3HR | -0.0529663 | -0.0689850 | -0.0503969 | -0.0201727 | 0.0438649 | 0.0418658 | 0.0521192 | 0.0587805 | 0.0557260 | 0.0474753 | 0.0858056 | 0.0246699 | 0.0877507 | 0.0735792 | 0.0637937 | 0.0495956 | 0.0415646 | 0.0576566 | 0.0726417 | 0.0650279 | 0.0630912 | 0.1034169 | 0.0158871 | 0.0844502 | 0.0687394 | -0.0615764 | 0.0310392 | 0.0519538 | -0.0557159 | -0.0430698 | -0.1066885 | 0.1198314 | 0.0685334 | 0.0100193 | 0.7309445 | 1.0000000 | 0.3894182 | 0.5124276 | 0.3029233 | 0.0214458 | -0.0003039 | 0.0083388 | 0.0443562 | 0.0597759 | -0.0068112 | 0.0143010 | 0.0121194 | 0.0396301 | 0.0312695 | -0.0311801 | -0.0131473 | 0.0329404 | 0.0208887 | -0.0022962 | 0.0293689 | -0.0176156 | -0.0367514 | -0.0072258 | 0.0030403 | -0.0148982 | 0.0376698 | -0.0243705 | 0.0044851 |
| SEP_SH_6HR | SEP_SH_6HR | 0.0313734 | 0.0231777 | 0.0048063 | 0.0484079 | -0.0683424 | -0.0697479 | -0.0725481 | -0.0562912 | -0.0637431 | -0.0250525 | -0.0192334 | -0.0291912 | 0.0093791 | 0.0265116 | -0.0494590 | -0.0522724 | -0.0658150 | -0.0681526 | -0.0510706 | -0.0549999 | -0.0194129 | -0.0099818 | -0.0303120 | 0.0072643 | 0.0292287 | -0.0354024 | -0.0124470 | -0.0201631 | 0.0160417 | 0.0018369 | -0.0990718 | 0.0629739 | 0.0455411 | 0.0697614 | 0.5744973 | 0.3894182 | 1.0000000 | 0.3849719 | 0.2338399 | -0.0161607 | -0.0334702 | -0.0156808 | 0.1359384 | 0.1422354 | -0.0041075 | -0.0631275 | -0.0279555 | -0.0515598 | -0.0787945 | -0.0854897 | -0.0659226 | -0.0727333 | -0.0738364 | -0.0368684 | -0.0345921 | 0.0185638 | -0.0310002 | -0.0467304 | -0.0030434 | -0.0228048 | 0.0032891 | -0.0713756 | 0.0072647 |
| SEV_SEP_3HR | SEV_SEP_3HR | -0.0322822 | -0.0375511 | -0.0227213 | -0.0083329 | 0.1642083 | 0.1295914 | 0.1605381 | 0.1606626 | 0.1782560 | 0.1732976 | 0.1869822 | 0.1003528 | 0.1897215 | 0.1760486 | 0.1800467 | 0.1983566 | 0.1433814 | 0.1772872 | 0.1823598 | 0.2122738 | 0.2014907 | 0.2012726 | 0.1023846 | 0.2115650 | 0.1933820 | -0.1265965 | -0.0175650 | 0.0361783 | -0.2007893 | -0.0844129 | -0.2279495 | 0.1834131 | 0.1111026 | 0.0854069 | 0.8329106 | 0.5124276 | 0.3849719 | 1.0000000 | 0.4694083 | 0.1935342 | 0.1476882 | 0.2318112 | 0.2111767 | 0.2098349 | -0.0375621 | -0.0091369 | 0.0251530 | 0.0425397 | -0.0246311 | -0.0663090 | -0.0624234 | -0.0956269 | -0.0378140 | -0.0141213 | -0.0257851 | -0.0434036 | -0.0699820 | -0.0642500 | -0.0485933 | -0.0700502 | -0.0047427 | -0.0993958 | -0.0154257 |
| SEV_SEP_6HR | SEV_SEP_6HR | -0.0278551 | -0.0014846 | 0.0212143 | 0.0002631 | 0.1503950 | 0.0899079 | 0.1368127 | 0.1423359 | 0.1758781 | 0.1422983 | 0.1376097 | 0.1010108 | 0.1544748 | 0.1358664 | 0.1591593 | 0.1847906 | 0.0975333 | 0.1643799 | 0.1625916 | 0.2084118 | 0.1664733 | 0.1514801 | 0.1022135 | 0.1691943 | 0.1501468 | -0.0558505 | -0.1149175 | 0.0484998 | -0.1593730 | -0.0481731 | -0.1444805 | 0.1440580 | 0.0304354 | 0.0857646 | 0.6460143 | 0.3029233 | 0.2338399 | 0.4694083 | 1.0000000 | 0.0037384 | 0.0649257 | 0.0631441 | 0.2522518 | 0.2083065 | -0.0109117 | -0.0072140 | 0.0299786 | 0.0287674 | -0.0152374 | -0.0457369 | -0.0572546 | -0.0824732 | -0.0074196 | -0.0351047 | -0.0587674 | -0.0265019 | -0.0400763 | -0.0886155 | -0.0494623 | -0.0377753 | -0.0179532 | -0.0875069 | -0.0092648 |
| STK_02 | STK_02 | -0.0434539 | 0.0066866 | 0.0415201 | 0.0242322 | 0.2176041 | 0.1940882 | 0.1201354 | 0.1358181 | 0.2091327 | 0.2157600 | 0.0367533 | 0.0765341 | 0.2160677 | 0.2348521 | 0.1860190 | 0.2367758 | 0.1984598 | 0.1340445 | 0.1647114 | 0.2236306 | 0.2341396 | 0.0455756 | 0.0817038 | 0.2211843 | 0.2439658 | -0.0983294 | 0.0947908 | 0.1318005 | 0.0723310 | -0.0076033 | -0.0066518 | 0.0821585 | 0.0653732 | 0.1450732 | 0.0976908 | 0.0214458 | -0.0161607 | 0.1935342 | 0.0037384 | 1.0000000 | 0.3266488 | 0.8021725 | 0.4631419 | 0.4148180 | -0.0319671 | -0.0518587 | -0.0534076 | 0.0093937 | -0.0939059 | -0.0905922 | -0.0044494 | -0.0746093 | 0.0129386 | -0.0292526 | 0.0219699 | -0.0138235 | -0.0675289 | 0.0183344 | -0.0210595 | 0.0392025 | -0.0065909 | -0.0294786 | -0.1317922 |
| STK_05 | STK_05 | -0.0205857 | -0.0801055 | -0.0867129 | -0.0093595 | 0.1753654 | 0.1763581 | 0.1413712 | 0.1964279 | 0.2034132 | 0.1380616 | 0.0909053 | 0.1069590 | 0.1589966 | 0.1378362 | 0.1945060 | 0.2012691 | 0.1920243 | 0.1572709 | 0.2084062 | 0.2431733 | 0.1543470 | 0.1000540 | 0.1098322 | 0.1738457 | 0.1443141 | 0.0254940 | 0.0304334 | 0.1020708 | -0.1190028 | -0.0476864 | -0.0291353 | 0.1352184 | 0.0263917 | 0.0858291 | 0.0923975 | -0.0003039 | -0.0334702 | 0.1476882 | 0.0649257 | 0.3266488 | 1.0000000 | 0.2500620 | 0.5880079 | 0.6976272 | 0.0512645 | 0.0331009 | -0.0155739 | 0.1007144 | -0.0037918 | -0.0246261 | 0.0132861 | -0.0270607 | -0.0072465 | 0.0776413 | 0.0066697 | -0.0467887 | -0.0263715 | -0.0426109 | -0.0270503 | 0.0071556 | 0.0285160 | -0.0154089 | -0.0328044 |
| STK_06 | STK_06 | -0.0425563 | -0.0101158 | 0.0212012 | 0.0377761 | 0.1870897 | 0.1579604 | 0.0905514 | 0.1045092 | 0.1930126 | 0.1802407 | 0.0153703 | 0.0440800 | 0.2063053 | 0.2020601 | 0.1646616 | 0.2088467 | 0.1718119 | 0.1065507 | 0.1450104 | 0.2738285 | 0.2096414 | 0.0284015 | 0.0437557 | 0.2315523 | 0.2442083 | -0.0925059 | 0.0831624 | 0.0900367 | 0.0948584 | 0.0158359 | 0.0057888 | 0.0401311 | 0.0285800 | 0.1347354 | 0.1245526 | 0.0083388 | -0.0156808 | 0.2318112 | 0.0631441 | 0.8021725 | 0.2500620 | 1.0000000 | 0.4774227 | 0.5132722 | -0.0207810 | -0.0497188 | -0.0477381 | -0.0086007 | -0.0990850 | -0.0787235 | -0.0203047 | -0.0479955 | -0.0035153 | -0.0646189 | 0.0144757 | -0.0298356 | -0.0454564 | 0.0343065 | -0.0293121 | 0.0542351 | 0.0085622 | -0.0284310 | -0.1296356 |
| VTE_1 | VTE_1 | 0.0039309 | 0.0654668 | 0.0866767 | 0.0503681 | -0.0859359 | -0.0887946 | -0.0996751 | -0.0999738 | 0.0235381 | -0.0364693 | -0.0848392 | -0.0897193 | -0.0348377 | 0.0037855 | -0.0552836 | -0.0544304 | -0.0972506 | -0.1148325 | -0.1112139 | 0.0383041 | -0.0268317 | -0.0880614 | -0.0886019 | -0.0248375 | 0.0058311 | 0.0761139 | 0.0241622 | 0.0906329 | 0.2344307 | 0.1235925 | 0.0805708 | 0.2485615 | 0.1567526 | -0.0563373 | 0.2303117 | 0.0443562 | 0.1359384 | 0.2111767 | 0.2522518 | 0.4631419 | 0.5880079 | 0.4774227 | 1.0000000 | 0.8736490 | -0.0526925 | -0.0493931 | -0.0282911 | -0.1051171 | -0.1710235 | -0.1238916 | -0.0378500 | -0.1180160 | -0.0262166 | -0.0522876 | -0.0577213 | -0.0037803 | -0.0301775 | -0.0317534 | -0.0440578 | 0.0086141 | 0.0326658 | -0.0475558 | -0.1256363 |
| VTE_2 | VTE_2 | 0.0707936 | 0.1064994 | 0.0781368 | 0.0677007 | -0.0029321 | -0.0231504 | -0.0477865 | -0.0151359 | 0.0288319 | 0.0393084 | -0.0027352 | -0.0754020 | 0.0691782 | 0.0981665 | 0.0051809 | 0.0173084 | -0.0314520 | -0.0405641 | -0.0255467 | 0.0559072 | 0.0534239 | -0.0051760 | -0.0634120 | 0.0775236 | 0.1184210 | 0.0321584 | -0.0151698 | 0.0058144 | 0.0629824 | 0.0408574 | -0.0317460 | 0.1664553 | 0.0271825 | 0.1913961 | 0.2029424 | 0.0597759 | 0.1422354 | 0.2098349 | 0.2083065 | 0.4148180 | 0.6976272 | 0.5132722 | 0.8736490 | 1.0000000 | -0.0073599 | -0.0995881 | -0.1188383 | -0.1492708 | -0.1770482 | -0.1181448 | -0.0713006 | -0.1762469 | -0.0469516 | -0.1120007 | -0.0691325 | 0.0036343 | -0.0247178 | -0.0036828 | 0.0009109 | 0.0110118 | -0.0279567 | -0.0666219 | 0.0217206 |
| Score_COMP_HIP_KNEE | Score_COMP_HIP_KNEE | 0.4350513 | 0.3208550 | 0.0742579 | -0.1459884 | -0.0761301 | -0.0615070 | -0.0405313 | -0.0450744 | -0.0990420 | -0.1067242 | -0.0571772 | -0.0250746 | -0.0967251 | -0.1246042 | -0.0822439 | -0.0796721 | -0.0680571 | -0.0605862 | -0.0557335 | -0.1078990 | -0.1051209 | -0.0539829 | -0.0341816 | -0.1091775 | -0.1332048 | 0.0673396 | -0.0510683 | -0.0212916 | -0.0293698 | 0.0028501 | 0.0214909 | 0.0398944 | -0.0096464 | -0.0081923 | -0.0347567 | -0.0068112 | -0.0041075 | -0.0375621 | -0.0109117 | -0.0319671 | 0.0512645 | -0.0207810 | -0.0526925 | -0.0073599 | 1.0000000 | 0.0830479 | -0.0203930 | -0.0007242 | 0.0241066 | 0.0211621 | 0.0498557 | 0.0038509 | 0.0505415 | 0.0577776 | 0.0540124 | 0.0813038 | 0.1279724 | 0.1458258 | 0.1334619 | 0.0498603 | 0.0433809 | 0.1604802 | 0.3410864 |
| Score_MORT_30_AMI | Score_MORT_30_AMI | 0.0392653 | 0.0074065 | -0.0287661 | -0.1664294 | -0.0479940 | -0.0650654 | -0.0177776 | -0.0510480 | -0.0682603 | -0.1098730 | -0.0630079 | 0.0428372 | -0.0951559 | -0.0981817 | -0.0710775 | -0.0452192 | -0.0565823 | -0.0215130 | -0.0641464 | -0.0733482 | -0.1115171 | -0.0624749 | 0.0366437 | -0.0952111 | -0.1095716 | 0.0579101 | -0.0869890 | -0.0165321 | -0.0678837 | -0.0296718 | 0.0376811 | 0.0034495 | -0.0600569 | -0.0643353 | -0.0154393 | 0.0143010 | -0.0631275 | -0.0091369 | -0.0072140 | -0.0518587 | 0.0331009 | -0.0497188 | -0.0493931 | -0.0995881 | 0.0830479 | 1.0000000 | 0.2498600 | 0.3407616 | 0.3309425 | 0.2222539 | 0.0415523 | 0.2105379 | 0.0885083 | 0.1010348 | 0.0889343 | 0.1066619 | 0.1037006 | 0.0492328 | 0.0467554 | 0.0454462 | 0.0297688 | 0.1129695 | 0.0591548 |
| Score_MORT_30_COPD | Score_MORT_30_COPD | -0.0452304 | -0.0794948 | -0.0683850 | -0.0875397 | -0.0124762 | -0.0163367 | 0.0302290 | -0.0130589 | 0.0034724 | -0.0673445 | -0.0352455 | 0.0722857 | -0.0338034 | -0.0266626 | -0.0009117 | -0.0003548 | -0.0027657 | 0.0433887 | -0.0091794 | 0.0081764 | -0.0514076 | -0.0255442 | 0.0773892 | -0.0230632 | -0.0262870 | -0.0537002 | -0.1128278 | -0.0616051 | -0.1527290 | -0.0977614 | -0.0446228 | 0.0135325 | 0.0081252 | -0.0362573 | 0.0197865 | 0.0121194 | -0.0279555 | 0.0251530 | 0.0299786 | -0.0534076 | -0.0155739 | -0.0477381 | -0.0282911 | -0.1188383 | -0.0203930 | 0.2498600 | 1.0000000 | 0.3844105 | 0.3710744 | 0.2038243 | -0.0069743 | 0.1713379 | 0.0478268 | 0.0397571 | 0.0429090 | 0.0320669 | 0.0426574 | -0.0532586 | 0.0026944 | 0.0734846 | 0.0340007 | 0.0140214 | -0.0406696 |
| Score_MORT_30_HF | Score_MORT_30_HF | -0.0577013 | -0.1067828 | -0.0967183 | -0.1319899 | 0.1078488 | 0.0722015 | 0.1568119 | 0.0644025 | 0.1194712 | 0.0151872 | 0.0665845 | 0.1510853 | 0.0240132 | -0.0005694 | 0.0947303 | 0.1228267 | 0.0845920 | 0.1663036 | 0.0704411 | 0.1281267 | 0.0259241 | 0.0677998 | 0.1553155 | 0.0300940 | -0.0003806 | -0.1136600 | -0.1245435 | -0.0010634 | -0.2195933 | -0.1375348 | -0.0767863 | 0.0045056 | 0.0099705 | 0.0171605 | 0.0170609 | 0.0396301 | -0.0515598 | 0.0425397 | 0.0287674 | 0.0093937 | 0.1007144 | -0.0086007 | -0.1051171 | -0.1492708 | -0.0007242 | 0.3407616 | 0.3844105 | 1.0000000 | 0.4479367 | 0.3147371 | 0.0371596 | 0.2556384 | 0.0679149 | 0.1051698 | 0.0707269 | 0.0383771 | 0.0362529 | -0.0300702 | -0.0086832 | 0.0647245 | 0.0342374 | 0.0465081 | -0.0350247 |
| Score_MORT_30_PN | Score_MORT_30_PN | -0.0046351 | -0.0985660 | -0.1319780 | -0.1252656 | 0.0183801 | -0.0021513 | 0.0384967 | -0.0046993 | -0.0343594 | -0.0880905 | 0.0009844 | 0.0902457 | -0.0613852 | -0.1107731 | -0.0145406 | 0.0156651 | 0.0121242 | 0.0511012 | -0.0036037 | -0.0315360 | -0.0829950 | -0.0009274 | 0.0950135 | -0.0702915 | -0.1100101 | -0.0389080 | -0.1523779 | -0.0761104 | -0.1858291 | -0.0578145 | -0.0559254 | -0.0210473 | -0.0536184 | -0.0204107 | -0.0335808 | 0.0312695 | -0.0787945 | -0.0246311 | -0.0152374 | -0.0939059 | -0.0037918 | -0.0990850 | -0.1710235 | -0.1770482 | 0.0241066 | 0.3309425 | 0.3710744 | 0.4479367 | 1.0000000 | 0.3042563 | 0.0303815 | 0.2301195 | 0.0543554 | 0.0884315 | 0.0217880 | 0.0237048 | 0.0704445 | 0.0089560 | 0.0393676 | 0.0464407 | 0.0029691 | 0.0661595 | -0.0062985 |
| Score_MORT_30_STK | Score_MORT_30_STK | -0.0025521 | -0.0376746 | -0.0478311 | -0.1373235 | -0.0132887 | -0.0165143 | 0.0435642 | 0.0257903 | 0.0159051 | -0.0653713 | -0.0651509 | 0.0337813 | -0.0814552 | -0.1033645 | -0.0275058 | -0.0170135 | 0.0058744 | 0.0476323 | 0.0323273 | 0.0086356 | -0.0564195 | -0.0795298 | 0.0255069 | -0.0760295 | -0.1074875 | 0.0005912 | -0.0988833 | 0.0146397 | -0.0905644 | -0.0463395 | 0.0198257 | -0.0614632 | -0.0251654 | -0.0804707 | -0.0769699 | -0.0311801 | -0.0854897 | -0.0663090 | -0.0457369 | -0.0905922 | -0.0246261 | -0.0787235 | -0.1238916 | -0.1181448 | 0.0211621 | 0.2222539 | 0.2038243 | 0.3147371 | 0.3042563 | 1.0000000 | 0.0687216 | 0.2380935 | 0.0878847 | 0.1014879 | 0.0674377 | 0.0622532 | 0.0725381 | 0.0474896 | 0.0513975 | 0.0492194 | 0.0625191 | 0.1142992 | -0.0272101 |
| Score_PSI_03 | Score_PSI_03 | -0.0058883 | -0.0037334 | 0.0027232 | -0.0376057 | -0.0219430 | -0.0016914 | -0.0192053 | -0.0221085 | 0.0012755 | -0.0300329 | -0.0629368 | -0.0457618 | -0.0490180 | -0.0387585 | -0.0283146 | -0.0236955 | 0.0019106 | -0.0223315 | -0.0128473 | 0.0049870 | -0.0211932 | -0.0709592 | -0.0454692 | -0.0499063 | -0.0363290 | 0.0602674 | 0.0943953 | 0.0451508 | 0.0583644 | 0.0181970 | 0.0648778 | -0.0570477 | -0.0032584 | -0.0287158 | -0.0797702 | -0.0131473 | -0.0659226 | -0.0624234 | -0.0572546 | -0.0044494 | 0.0132861 | -0.0203047 | -0.0378500 | -0.0713006 | 0.0498557 | 0.0415523 | -0.0069743 | 0.0371596 | 0.0303815 | 0.0687216 | 1.0000000 | 0.1353085 | 0.0601750 | 0.0636661 | 0.1407342 | 0.0386211 | 0.0114365 | 0.1186788 | 0.0298580 | 0.0596798 | 0.0999683 | 0.7496827 | 0.0086745 |
| Score_PSI_04 | Score_PSI_04 | 0.0129772 | -0.0449077 | -0.0749480 | -0.0681506 | -0.0320167 | -0.0041080 | -0.0016542 | -0.0185639 | 0.0047573 | -0.0817158 | -0.1272277 | -0.0343542 | -0.0852573 | -0.0857042 | -0.0547617 | -0.0303324 | -0.0063422 | -0.0117228 | -0.0052418 | 0.0228481 | -0.0638071 | -0.1182074 | -0.0450913 | -0.0948401 | -0.1008569 | -0.0541503 | 0.0417007 | 0.0601831 | 0.0638412 | 0.0046272 | 0.1019307 | -0.0326986 | 0.0312780 | -0.0995591 | -0.0937443 | 0.0329404 | -0.0727333 | -0.0956269 | -0.0824732 | -0.0746093 | -0.0270607 | -0.0479955 | -0.1180160 | -0.1762469 | 0.0038509 | 0.2105379 | 0.1713379 | 0.2556384 | 0.2301195 | 0.2380935 | 0.1353085 | 1.0000000 | 0.0601419 | 0.0870693 | 0.1059485 | 0.0523892 | 0.0649032 | 0.0782559 | 0.0123489 | 0.0652098 | 0.1018205 | 0.1589978 | -0.0766302 |
| Score_PSI_06 | Score_PSI_06 | 0.0156305 | 0.0154891 | 0.0017920 | -0.0571022 | -0.0184090 | 0.0301520 | 0.0274588 | 0.0273930 | 0.0312923 | -0.0009316 | -0.0390292 | -0.0062045 | -0.0166476 | -0.0221487 | 0.0061083 | -0.0019546 | 0.0334780 | 0.0152153 | 0.0247052 | 0.0256121 | 0.0098029 | -0.0369162 | -0.0062861 | -0.0054742 | -0.0193730 | 0.0551291 | 0.0225916 | 0.0544576 | 0.0187794 | 0.0392571 | 0.0452860 | -0.0515403 | 0.0059688 | -0.0603629 | -0.0339321 | 0.0208887 | -0.0738364 | -0.0378140 | -0.0074196 | 0.0129386 | -0.0072465 | -0.0035153 | -0.0262166 | -0.0469516 | 0.0505415 | 0.0885083 | 0.0478268 | 0.0679149 | 0.0543554 | 0.0878847 | 0.0601750 | 0.0601419 | 1.0000000 | 0.0724291 | 0.1014588 | 0.0516246 | 0.0351464 | 0.1431056 | 0.0509831 | 0.0527115 | 0.0910520 | 0.1455340 | 0.0456525 |
| Score_PSI_08 | Score_PSI_08 | 0.0386634 | -0.0214412 | -0.0595834 | -0.1292021 | 0.0046992 | -0.0171646 | 0.0159553 | -0.0011887 | -0.0120380 | -0.0348072 | 0.0016051 | 0.0093687 | -0.0686735 | -0.0800058 | -0.0156145 | -0.0080622 | -0.0028885 | 0.0187552 | -0.0039459 | -0.0176853 | -0.0381375 | 0.0027541 | 0.0051695 | -0.0590998 | -0.0826240 | -0.0211509 | -0.0272232 | -0.0311579 | -0.0806516 | -0.0457654 | 0.0102293 | 0.0269822 | -0.0199006 | -0.0017558 | -0.0179990 | -0.0022962 | -0.0368684 | -0.0141213 | -0.0351047 | -0.0292526 | 0.0776413 | -0.0646189 | -0.0522876 | -0.1120007 | 0.0577776 | 0.1010348 | 0.0397571 | 0.1051698 | 0.0884315 | 0.1014879 | 0.0636661 | 0.0870693 | 0.0724291 | 1.0000000 | 0.0052449 | -0.0360093 | 0.0198090 | 0.0394605 | 0.0093444 | 0.0228045 | 0.0127268 | 0.0624052 | -0.0041983 |
| Score_PSI_09 | Score_PSI_09 | -0.0459321 | -0.0182303 | 0.0136214 | -0.0138771 | 0.0835767 | 0.0956403 | 0.0859168 | 0.0897240 | 0.0883277 | 0.0827573 | -0.0069280 | 0.0220979 | 0.0710883 | 0.0787681 | 0.0913137 | 0.0845264 | 0.0956884 | 0.0790559 | 0.0793646 | 0.0859133 | 0.0825062 | -0.0109302 | 0.0262747 | 0.0707774 | 0.0862428 | -0.0336614 | 0.0549990 | 0.0899361 | 0.0224833 | -0.0128057 | 0.0426036 | -0.0140788 | 0.0150699 | -0.0043396 | -0.0245784 | 0.0293689 | -0.0345921 | -0.0257851 | -0.0587674 | 0.0219699 | 0.0066697 | 0.0144757 | -0.0577213 | -0.0691325 | 0.0540124 | 0.0889343 | 0.0429090 | 0.0707269 | 0.0217880 | 0.0674377 | 0.1407342 | 0.1059485 | 0.1014588 | 0.0052449 | 1.0000000 | 0.0885278 | 0.0680540 | 0.1732337 | 0.0519119 | 0.1207438 | 0.2197254 | 0.2331017 | -0.0237660 |
| Score_PSI_10 | Score_PSI_10 | 0.0099239 | 0.0710046 | 0.0894074 | -0.0604643 | -0.0402520 | -0.0267891 | -0.0262995 | -0.0322016 | -0.0190535 | -0.0399586 | -0.0851994 | -0.0051357 | -0.0270735 | 0.0075443 | -0.0452172 | -0.0270956 | -0.0236698 | -0.0330533 | -0.0452983 | -0.0145875 | -0.0316508 | -0.0753999 | -0.0054371 | -0.0157615 | -0.0105068 | 0.0315830 | 0.0009222 | 0.0412713 | 0.0544528 | 0.0031479 | 0.0343100 | -0.0045339 | 0.0331569 | -0.0382369 | -0.0390884 | -0.0176156 | 0.0185638 | -0.0434036 | -0.0265019 | -0.0138235 | -0.0467887 | -0.0298356 | -0.0037803 | 0.0036343 | 0.0813038 | 0.1066619 | 0.0320669 | 0.0383771 | 0.0237048 | 0.0622532 | 0.0386211 | 0.0523892 | 0.0516246 | -0.0360093 | 0.0885278 | 1.0000000 | 0.1626632 | 0.1079488 | 0.2303938 | 0.0453739 | 0.0830134 | 0.2670390 | 0.0497447 |
| Score_PSI_11 | Score_PSI_11 | 0.1103031 | 0.1130121 | 0.0639191 | -0.0502829 | -0.1459187 | -0.1367919 | -0.1292050 | -0.1375039 | -0.1484276 | -0.1668264 | -0.1286917 | -0.0772394 | -0.1585705 | -0.1576927 | -0.1540619 | -0.1428877 | -0.1414366 | -0.1408040 | -0.1557561 | -0.1539039 | -0.1629600 | -0.1233582 | -0.0789391 | -0.1615833 | -0.1710237 | 0.1430203 | -0.0909811 | -0.0625676 | -0.0076797 | 0.0074122 | 0.0397075 | 0.0279871 | -0.0837910 | 0.0137283 | -0.0564170 | -0.0367514 | -0.0310002 | -0.0699820 | -0.0400763 | -0.0675289 | -0.0263715 | -0.0454564 | -0.0301775 | -0.0247178 | 0.1279724 | 0.1037006 | 0.0426574 | 0.0362529 | 0.0704445 | 0.0725381 | 0.0114365 | 0.0649032 | 0.0351464 | 0.0198090 | 0.0680540 | 0.1626632 | 1.0000000 | 0.1172504 | 0.2506376 | -0.0093577 | 0.0464067 | 0.5858033 | 0.1441986 |
| Score_PSI_12 | Score_PSI_12 | 0.0939499 | 0.1047402 | 0.0654692 | -0.0445391 | -0.0669148 | -0.0390690 | -0.0865925 | -0.0537461 | -0.0612852 | -0.0670446 | -0.0807406 | -0.0730231 | -0.0643414 | -0.0393015 | -0.0738343 | -0.0663743 | -0.0380979 | -0.0953948 | -0.0685223 | -0.0536226 | -0.0614646 | -0.0813748 | -0.0813685 | -0.0674588 | -0.0510987 | 0.1038617 | 0.1091949 | 0.0594933 | 0.1653812 | 0.0486693 | 0.0778917 | -0.0606628 | -0.0108546 | -0.0380145 | -0.0757690 | -0.0072258 | -0.0467304 | -0.0642500 | -0.0886155 | 0.0183344 | -0.0426109 | 0.0343065 | -0.0317534 | -0.0036828 | 0.1458258 | 0.0492328 | -0.0532586 | -0.0300702 | 0.0089560 | 0.0474896 | 0.1186788 | 0.0782559 | 0.1431056 | 0.0394605 | 0.1732337 | 0.1079488 | 0.1172504 | 1.0000000 | 0.1742084 | 0.0522204 | 0.1358951 | 0.3821290 | 0.0655557 |
| Score_PSI_13 | Score_PSI_13 | 0.1262199 | 0.1193336 | 0.0603447 | -0.0694840 | -0.1424419 | -0.1430591 | -0.1330731 | -0.1257130 | -0.1424055 | -0.1593474 | -0.1231640 | -0.1033157 | -0.1472927 | -0.1297623 | -0.1614933 | -0.1459851 | -0.1394736 | -0.1388091 | -0.1386420 | -0.1430776 | -0.1636005 | -0.1261494 | -0.1096072 | -0.1458284 | -0.1364494 | 0.0817412 | -0.0160408 | -0.0250714 | 0.0993570 | 0.0559662 | 0.0451515 | -0.0384416 | -0.0087678 | -0.0258097 | -0.0254596 | 0.0030403 | -0.0030434 | -0.0485933 | -0.0494623 | -0.0210595 | -0.0270503 | -0.0293121 | -0.0440578 | 0.0009109 | 0.1334619 | 0.0467554 | 0.0026944 | -0.0086832 | 0.0393676 | 0.0513975 | 0.0298580 | 0.0123489 | 0.0509831 | 0.0093444 | 0.0519119 | 0.2303938 | 0.2506376 | 0.1742084 | 1.0000000 | 0.0056987 | 0.0878105 | 0.4075564 | 0.0949467 |
| Score_PSI_14 | Score_PSI_14 | -0.0138581 | 0.0140012 | 0.0328153 | -0.0737703 | 0.0017770 | -0.0079719 | 0.0092188 | -0.0064032 | -0.0023314 | -0.0164980 | -0.0459005 | -0.0277664 | -0.0205437 | 0.0134380 | -0.0003415 | -0.0029199 | -0.0143285 | -0.0063199 | -0.0063967 | 0.0069610 | -0.0189605 | -0.0505286 | -0.0256155 | -0.0172231 | 0.0003983 | 0.0633107 | 0.0169512 | 0.0723872 | 0.0676972 | 0.0192234 | 0.0652307 | -0.0593032 | 0.0163779 | -0.0166146 | -0.0644595 | -0.0148982 | -0.0228048 | -0.0700502 | -0.0377753 | 0.0392025 | 0.0071556 | 0.0542351 | 0.0086141 | 0.0110118 | 0.0498603 | 0.0454462 | 0.0734846 | 0.0647245 | 0.0464407 | 0.0492194 | 0.0596798 | 0.0652098 | 0.0527115 | 0.0228045 | 0.1207438 | 0.0453739 | -0.0093577 | 0.0522204 | 0.0056987 | 1.0000000 | 0.1176726 | 0.0783006 | -0.0181150 |
| Score_PSI_15 | Score_PSI_15 | -0.0019014 | -0.0158282 | -0.0242059 | -0.0514689 | 0.0329127 | 0.0359419 | 0.0480143 | 0.0443475 | 0.0662158 | 0.0357574 | -0.0007875 | -0.0213211 | 0.0287970 | 0.0262619 | 0.0303266 | 0.0385911 | 0.0425664 | 0.0405341 | 0.0420468 | 0.0649921 | 0.0320446 | 0.0061946 | -0.0196124 | 0.0304634 | 0.0331751 | -0.0393782 | 0.0430812 | 0.0625753 | 0.0374530 | 0.0049833 | 0.0094012 | -0.0270588 | 0.0419068 | -0.0268805 | -0.0051105 | 0.0376698 | 0.0032891 | -0.0047427 | -0.0179532 | -0.0065909 | 0.0285160 | 0.0085622 | 0.0326658 | -0.0279567 | 0.0433809 | 0.0297688 | 0.0340007 | 0.0342374 | 0.0029691 | 0.0625191 | 0.0999683 | 0.1018205 | 0.0910520 | 0.0127268 | 0.2197254 | 0.0830134 | 0.0464067 | 0.1358951 | 0.0878105 | 0.1176726 | 1.0000000 | 0.2021298 | -0.0467071 |
| Score_PSI_90 | Score_PSI_90 | 0.0882354 | 0.0973882 | 0.0626347 | -0.0836880 | -0.1122411 | -0.0871039 | -0.1032756 | -0.1023631 | -0.0911960 | -0.1311288 | -0.1463724 | -0.0977954 | -0.1387904 | -0.1202753 | -0.1263561 | -0.1096342 | -0.0826332 | -0.1131291 | -0.1086247 | -0.0872152 | -0.1202132 | -0.1486299 | -0.1003157 | -0.1390570 | -0.1279752 | 0.1286484 | 0.0534106 | 0.0226639 | 0.0986077 | 0.0401196 | 0.0926476 | -0.0530033 | -0.0312757 | -0.0300979 | -0.1034135 | -0.0243705 | -0.0713756 | -0.0993958 | -0.0875069 | -0.0294786 | -0.0154089 | -0.0284310 | -0.0475558 | -0.0666219 | 0.1604802 | 0.1129695 | 0.0140214 | 0.0465081 | 0.0661595 | 0.1142992 | 0.7496827 | 0.1589978 | 0.1455340 | 0.0624052 | 0.2331017 | 0.2670390 | 0.5858033 | 0.3821290 | 0.4075564 | 0.0783006 | 0.2021298 | 1.0000000 | 0.1036455 |
| Payment_PAYM_90_HIP_KNEE | Payment_PAYM_90_HIP_KNEE | 0.2740999 | 0.2975679 | 0.1808580 | -0.1282785 | -0.2121841 | -0.1893123 | -0.1564699 | -0.1576660 | -0.2089917 | -0.1977109 | -0.0409475 | -0.0612456 | -0.2018848 | -0.2235957 | -0.1981220 | -0.2107792 | -0.1875503 | -0.1799323 | -0.1660056 | -0.2310210 | -0.2069970 | -0.0439537 | -0.0653730 | -0.2108956 | -0.2364653 | 0.1212071 | -0.0627505 | -0.0720431 | -0.0241965 | 0.0264108 | -0.0237710 | 0.0545394 | -0.0815209 | -0.0048449 | -0.0078309 | 0.0044851 | 0.0072647 | -0.0154257 | -0.0092648 | -0.1317922 | -0.0328044 | -0.1296356 | -0.1256363 | 0.0217206 | 0.3410864 | 0.0591548 | -0.0406696 | -0.0350247 | -0.0062985 | -0.0272101 | 0.0086745 | -0.0766302 | 0.0456525 | -0.0041983 | -0.0237660 | 0.0497447 | 0.1441986 | 0.0655557 | 0.0949467 | -0.0181150 | -0.0467071 | 0.1036455 | 1.0000000 |
Feature Preprocessing
Variable Encoding (SE)
Identify Categorical Variables
# Create function to find categorical variables
is_categorical <- function(x) is.factor(x) | is.character(x)
# Apply function to all variables in the dataset
categorical_vars <- sapply(HipKneeClean, is_categorical)
# Print the names of all categorical variables
categorical <- names(HipKneeClean)[categorical_vars]
categorical## [1] "FacilityId" "EDV" "FacilityName" "State"Dummy encode the EDV column
# Define the encoding mapping (ignore NAs for now)
encoding_map <- c(
'low' = 1,
'medium' = 2,
'high' = 3,
'very high' = 4
)
# Dummy encoding used due to ordinal nature of this data
# Create a copy of HipKneeClean and name it HipKneeTrain to separate cleaned dataset and the training dataset
HipKneeTrain <- HipKneeClean %>%
mutate(EDV = recode(EDV, !!!encoding_map))
# Print first 20 rows of EDV column in HipKneeClean and HipKneeTrain to ensure proper encoding
cat("HipKneeClean")## HipKneeCleanprint(head(HipKneeClean$EDV, 20))## [1] "high" "high" "high" "low" "low" "high"
## [7] "low" "medium" "low" "medium" "low" "low"
## [13] "high" "high" "very high" "very high" "low" "high"
## [19] "low" "very high"cat("HipKneeTrain")## HipKneeTrainprint(head(HipKneeTrain$EDV, 20))## [1] 3 3 3 1 1 3 1 2 1 2 1 1 3 3 4 4 1 3 1 4Encode each state in alphabetical order
# Manually map out each state with their respective code in alphabetical order with a preceding 0 to make data non-ordinal
state_mapping <- c(
"AL" = "001",
"AK" = "002",
"AZ" = "003",
"AR" = "004",
"CA" = "005",
"CO" = "006",
"CT" = "007",
"DE" = "008",
"FL" = "009",
"GA" = "010",
"HI" = "011",
"ID" = "012",
"IL" = "013",
"IN" = "014",
"IA" = "015",
"KS" = "016",
"KY" = "017",
"LA" = "018",
"ME" = "019",
"MD" = "020",
"MA" = "021",
"MI" = "022",
"MN" = "023",
"MS" = "024",
"MO" = "025",
"MT" = "026",
"NE" = "027",
"NV" = "028",
"NH" = "029",
"NJ" = "030",
"NM" = "031",
"NY" = "032",
"NC" = "033",
"ND" = "034",
"OH" = "035",
"OK" = "036",
"OR" = "037",
"PA" = "038",
"RI" = "039",
"SC" = "040",
"SD" = "041",
"TN" = "042",
"TX" = "043",
"UT" = "044",
"VT" = "045",
"VA" = "046",
"WA" = "047",
"WV" = "048",
"WI" = "049",
"WY" = "050"
)
# Create new "StateCode" column with the encoded values
HipKneeTrain <- HipKneeTrain %>%
mutate(StateCode = state_mapping[State])
# Print 100 rows of the "State" and "StateCode" columns to ensure accuracy
print("State and StateCode Columns")## [1] "State and StateCode Columns"print(head(HipKneeTrain[c("State", "StateCode")], 100))## # A tibble: 100 × 2
## State StateCode
## <chr> <chr>
## 1 AL 001
## 2 AL 001
## 3 AL 001
## 4 AL 001
## 5 AL 001
## 6 AL 001
## 7 AL 001
## 8 AL 001
## 9 AL 001
## 10 AL 001
## # ℹ 90 more rows# Print all unique values in "StateCode" column to ensure accuracy
print("Unique StateCode Values")## [1] "Unique StateCode Values"print(unique(HipKneeTrain$StateCode))## [1] "001" "002" "003" "004" "005" "006" "007" "008" NA "009" "010" "011"
## [13] "012" "013" "014" "015" "016" "017" "018" "019" "020" "021" "022" "023"
## [25] "024" "025" "026" "027" "028" "029" "030" "031" "032" "033" "034" "035"
## [37] "036" "037" "038" "039" "040" "041" "042" "043" "044" "045" "046" "047"
## [49] "048" "049" "050"Collinearity and Feature Removal (SE)
Reassess collinearity with heatmap and correlation matrix
# Compute correlation matrix
cor_matrix <- cor(HipKneeTrain %>% select_if(is.numeric), use = "pairwise.complete.obs")
# Melt the correlation matrix into a long format
cor_melted <- melt(cor_matrix)
# Plot heatmap
ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), name = "Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Figure 5. Correlation Heatmap of Numeric Variables")
# Convert correlation matrix to df
cor_table <- as.data.frame(cor_matrix)
# Add variable names as a column
cor_table$Variable <- rownames(cor_table)
# Reorder columns
cor_table <- cor_table %>%
select(Variable, everything())
# Print table
cor_table %>%
kable(caption = "Table 8. Correlation Coefficients Table") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | PredictedReadmissionRate_HIP_KNEE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | EDV | HCP_COVID_19 | IMM_3 | OP_18b | OP_29 | SAFE_USE_OF_OPIOIDS | VTE_1 | Score_COMP_HIP_KNEE | Score_MORT_30_AMI | Score_MORT_30_COPD | Score_MORT_30_HF | Score_MORT_30_PN | Score_MORT_30_STK | Score_PSI_03 | Score_PSI_04 | Score_PSI_06 | Score_PSI_08 | Score_PSI_09 | Score_PSI_10 | Score_PSI_11 | Score_PSI_12 | Score_PSI_13 | Score_PSI_14 | Score_PSI_15 | Payment_PAYM_90_HIP_KNEE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PredictedReadmissionRate_HIP_KNEE | PredictedReadmissionRate_HIP_KNEE | 1.0000000 | -0.2060912 | 0.1986939 | -0.0563082 | -0.0028840 | 0.1295727 | -0.0106510 | 0.1063002 | 0.0654668 | 0.3208550 | 0.0074065 | -0.0794948 | -0.1067828 | -0.0985660 | -0.0376746 | -0.0037334 | -0.0449077 | 0.0154891 | -0.0214412 | -0.0182303 | 0.0710046 | 0.1130121 | 0.1047402 | 0.1193336 | 0.0140012 | -0.0158282 | 0.2975679 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | -0.2060912 | 1.0000000 | -0.2262341 | 0.0302154 | 0.2182505 | -0.2448842 | 0.0865028 | 0.1100002 | -0.0248375 | -0.1091775 | -0.0952111 | -0.0230632 | 0.0300940 | -0.0702915 | -0.0760295 | -0.0499063 | -0.0948401 | -0.0054742 | -0.0590998 | 0.0707774 | -0.0157615 | -0.1615833 | -0.0674588 | -0.1458284 | -0.0172231 | 0.0304634 | -0.2108956 |
| EDV | EDV | 0.1986939 | -0.2262341 | 1.0000000 | 0.1599806 | 0.0038674 | 0.5918897 | 0.0603877 | -0.1223889 | 0.2992859 | -0.0240093 | -0.0687401 | -0.0840621 | -0.2588739 | -0.1904351 | -0.0754281 | 0.0292819 | -0.0438108 | -0.0308989 | -0.1637017 | -0.0181523 | 0.0837980 | 0.0455190 | 0.0749218 | 0.0724936 | 0.0399935 | 0.0144400 | 0.0053946 |
| HCP_COVID_19 | HCP_COVID_19 | -0.0563082 | 0.0302154 | 0.1599806 | 1.0000000 | 0.3203622 | 0.2574291 | 0.1067941 | -0.0812735 | 0.0241622 | -0.0510683 | -0.0869890 | -0.1128278 | -0.1245435 | -0.1523779 | -0.0988833 | 0.0943953 | 0.0417007 | 0.0225916 | -0.0272232 | 0.0549990 | 0.0009222 | -0.0909811 | 0.1091949 | -0.0160408 | 0.0169512 | 0.0430812 | -0.0627505 |
| IMM_3 | IMM_3 | -0.0028840 | 0.2182505 | 0.0038674 | 0.3203622 | 1.0000000 | 0.1105628 | 0.1317922 | 0.0410289 | 0.0906329 | -0.0212916 | -0.0165321 | -0.0616051 | -0.0010634 | -0.0761104 | 0.0146397 | 0.0451508 | 0.0601831 | 0.0544576 | -0.0311579 | 0.0899361 | 0.0412713 | -0.0625676 | 0.0594933 | -0.0250714 | 0.0723872 | 0.0625753 | -0.0720431 |
| OP_18b | OP_18b | 0.1295727 | -0.2448842 | 0.5918897 | 0.2574291 | 0.1105628 | 1.0000000 | 0.0506067 | -0.1400845 | 0.2344307 | -0.0293698 | -0.0678837 | -0.1527290 | -0.2195933 | -0.1858291 | -0.0905644 | 0.0583644 | 0.0638412 | 0.0187794 | -0.0806516 | 0.0224833 | 0.0544528 | -0.0076797 | 0.1653812 | 0.0993570 | 0.0676972 | 0.0374530 | -0.0241965 |
| OP_29 | OP_29 | -0.0106510 | 0.0865028 | 0.0603877 | 0.1067941 | 0.1317922 | 0.0506067 | 1.0000000 | -0.0650231 | 0.1567526 | -0.0096464 | -0.0600569 | 0.0081252 | 0.0099705 | -0.0536184 | -0.0251654 | -0.0032584 | 0.0312780 | 0.0059688 | -0.0199006 | 0.0150699 | 0.0331569 | -0.0837910 | -0.0108546 | -0.0087678 | 0.0163779 | 0.0419068 | -0.0815209 |
| SAFE_USE_OF_OPIOIDS | SAFE_USE_OF_OPIOIDS | 0.1063002 | 0.1100002 | -0.1223889 | -0.0812735 | 0.0410289 | -0.1400845 | -0.0650231 | 1.0000000 | -0.0563373 | -0.0081923 | -0.0643353 | -0.0362573 | 0.0171605 | -0.0204107 | -0.0804707 | -0.0287158 | -0.0995591 | -0.0603629 | -0.0017558 | -0.0043396 | -0.0382369 | 0.0137283 | -0.0380145 | -0.0258097 | -0.0166146 | -0.0268805 | -0.0048449 |
| VTE_1 | VTE_1 | 0.0654668 | -0.0248375 | 0.2992859 | 0.0241622 | 0.0906329 | 0.2344307 | 0.1567526 | -0.0563373 | 1.0000000 | -0.0526925 | -0.0493931 | -0.0282911 | -0.1051171 | -0.1710235 | -0.1238916 | -0.0378500 | -0.1180160 | -0.0262166 | -0.0522876 | -0.0577213 | -0.0037803 | -0.0301775 | -0.0317534 | -0.0440578 | 0.0086141 | 0.0326658 | -0.1256363 |
| Score_COMP_HIP_KNEE | Score_COMP_HIP_KNEE | 0.3208550 | -0.1091775 | -0.0240093 | -0.0510683 | -0.0212916 | -0.0293698 | -0.0096464 | -0.0081923 | -0.0526925 | 1.0000000 | 0.0830479 | -0.0203930 | -0.0007242 | 0.0241066 | 0.0211621 | 0.0498557 | 0.0038509 | 0.0505415 | 0.0577776 | 0.0540124 | 0.0813038 | 0.1279724 | 0.1458258 | 0.1334619 | 0.0498603 | 0.0433809 | 0.3410864 |
| Score_MORT_30_AMI | Score_MORT_30_AMI | 0.0074065 | -0.0952111 | -0.0687401 | -0.0869890 | -0.0165321 | -0.0678837 | -0.0600569 | -0.0643353 | -0.0493931 | 0.0830479 | 1.0000000 | 0.2498600 | 0.3407616 | 0.3309425 | 0.2222539 | 0.0415523 | 0.2105379 | 0.0885083 | 0.1010348 | 0.0889343 | 0.1066619 | 0.1037006 | 0.0492328 | 0.0467554 | 0.0454462 | 0.0297688 | 0.0591548 |
| Score_MORT_30_COPD | Score_MORT_30_COPD | -0.0794948 | -0.0230632 | -0.0840621 | -0.1128278 | -0.0616051 | -0.1527290 | 0.0081252 | -0.0362573 | -0.0282911 | -0.0203930 | 0.2498600 | 1.0000000 | 0.3844105 | 0.3710744 | 0.2038243 | -0.0069743 | 0.1713379 | 0.0478268 | 0.0397571 | 0.0429090 | 0.0320669 | 0.0426574 | -0.0532586 | 0.0026944 | 0.0734846 | 0.0340007 | -0.0406696 |
| Score_MORT_30_HF | Score_MORT_30_HF | -0.1067828 | 0.0300940 | -0.2588739 | -0.1245435 | -0.0010634 | -0.2195933 | 0.0099705 | 0.0171605 | -0.1051171 | -0.0007242 | 0.3407616 | 0.3844105 | 1.0000000 | 0.4479367 | 0.3147371 | 0.0371596 | 0.2556384 | 0.0679149 | 0.1051698 | 0.0707269 | 0.0383771 | 0.0362529 | -0.0300702 | -0.0086832 | 0.0647245 | 0.0342374 | -0.0350247 |
| Score_MORT_30_PN | Score_MORT_30_PN | -0.0985660 | -0.0702915 | -0.1904351 | -0.1523779 | -0.0761104 | -0.1858291 | -0.0536184 | -0.0204107 | -0.1710235 | 0.0241066 | 0.3309425 | 0.3710744 | 0.4479367 | 1.0000000 | 0.3042563 | 0.0303815 | 0.2301195 | 0.0543554 | 0.0884315 | 0.0217880 | 0.0237048 | 0.0704445 | 0.0089560 | 0.0393676 | 0.0464407 | 0.0029691 | -0.0062985 |
| Score_MORT_30_STK | Score_MORT_30_STK | -0.0376746 | -0.0760295 | -0.0754281 | -0.0988833 | 0.0146397 | -0.0905644 | -0.0251654 | -0.0804707 | -0.1238916 | 0.0211621 | 0.2222539 | 0.2038243 | 0.3147371 | 0.3042563 | 1.0000000 | 0.0687216 | 0.2380935 | 0.0878847 | 0.1014879 | 0.0674377 | 0.0622532 | 0.0725381 | 0.0474896 | 0.0513975 | 0.0492194 | 0.0625191 | -0.0272101 |
| Score_PSI_03 | Score_PSI_03 | -0.0037334 | -0.0499063 | 0.0292819 | 0.0943953 | 0.0451508 | 0.0583644 | -0.0032584 | -0.0287158 | -0.0378500 | 0.0498557 | 0.0415523 | -0.0069743 | 0.0371596 | 0.0303815 | 0.0687216 | 1.0000000 | 0.1353085 | 0.0601750 | 0.0636661 | 0.1407342 | 0.0386211 | 0.0114365 | 0.1186788 | 0.0298580 | 0.0596798 | 0.0999683 | 0.0086745 |
| Score_PSI_04 | Score_PSI_04 | -0.0449077 | -0.0948401 | -0.0438108 | 0.0417007 | 0.0601831 | 0.0638412 | 0.0312780 | -0.0995591 | -0.1180160 | 0.0038509 | 0.2105379 | 0.1713379 | 0.2556384 | 0.2301195 | 0.2380935 | 0.1353085 | 1.0000000 | 0.0601419 | 0.0870693 | 0.1059485 | 0.0523892 | 0.0649032 | 0.0782559 | 0.0123489 | 0.0652098 | 0.1018205 | -0.0766302 |
| Score_PSI_06 | Score_PSI_06 | 0.0154891 | -0.0054742 | -0.0308989 | 0.0225916 | 0.0544576 | 0.0187794 | 0.0059688 | -0.0603629 | -0.0262166 | 0.0505415 | 0.0885083 | 0.0478268 | 0.0679149 | 0.0543554 | 0.0878847 | 0.0601750 | 0.0601419 | 1.0000000 | 0.0724291 | 0.1014588 | 0.0516246 | 0.0351464 | 0.1431056 | 0.0509831 | 0.0527115 | 0.0910520 | 0.0456525 |
| Score_PSI_08 | Score_PSI_08 | -0.0214412 | -0.0590998 | -0.1637017 | -0.0272232 | -0.0311579 | -0.0806516 | -0.0199006 | -0.0017558 | -0.0522876 | 0.0577776 | 0.1010348 | 0.0397571 | 0.1051698 | 0.0884315 | 0.1014879 | 0.0636661 | 0.0870693 | 0.0724291 | 1.0000000 | 0.0052449 | -0.0360093 | 0.0198090 | 0.0394605 | 0.0093444 | 0.0228045 | 0.0127268 | -0.0041983 |
| Score_PSI_09 | Score_PSI_09 | -0.0182303 | 0.0707774 | -0.0181523 | 0.0549990 | 0.0899361 | 0.0224833 | 0.0150699 | -0.0043396 | -0.0577213 | 0.0540124 | 0.0889343 | 0.0429090 | 0.0707269 | 0.0217880 | 0.0674377 | 0.1407342 | 0.1059485 | 0.1014588 | 0.0052449 | 1.0000000 | 0.0885278 | 0.0680540 | 0.1732337 | 0.0519119 | 0.1207438 | 0.2197254 | -0.0237660 |
| Score_PSI_10 | Score_PSI_10 | 0.0710046 | -0.0157615 | 0.0837980 | 0.0009222 | 0.0412713 | 0.0544528 | 0.0331569 | -0.0382369 | -0.0037803 | 0.0813038 | 0.1066619 | 0.0320669 | 0.0383771 | 0.0237048 | 0.0622532 | 0.0386211 | 0.0523892 | 0.0516246 | -0.0360093 | 0.0885278 | 1.0000000 | 0.1626632 | 0.1079488 | 0.2303938 | 0.0453739 | 0.0830134 | 0.0497447 |
| Score_PSI_11 | Score_PSI_11 | 0.1130121 | -0.1615833 | 0.0455190 | -0.0909811 | -0.0625676 | -0.0076797 | -0.0837910 | 0.0137283 | -0.0301775 | 0.1279724 | 0.1037006 | 0.0426574 | 0.0362529 | 0.0704445 | 0.0725381 | 0.0114365 | 0.0649032 | 0.0351464 | 0.0198090 | 0.0680540 | 0.1626632 | 1.0000000 | 0.1172504 | 0.2506376 | -0.0093577 | 0.0464067 | 0.1441986 |
| Score_PSI_12 | Score_PSI_12 | 0.1047402 | -0.0674588 | 0.0749218 | 0.1091949 | 0.0594933 | 0.1653812 | -0.0108546 | -0.0380145 | -0.0317534 | 0.1458258 | 0.0492328 | -0.0532586 | -0.0300702 | 0.0089560 | 0.0474896 | 0.1186788 | 0.0782559 | 0.1431056 | 0.0394605 | 0.1732337 | 0.1079488 | 0.1172504 | 1.0000000 | 0.1742084 | 0.0522204 | 0.1358951 | 0.0655557 |
| Score_PSI_13 | Score_PSI_13 | 0.1193336 | -0.1458284 | 0.0724936 | -0.0160408 | -0.0250714 | 0.0993570 | -0.0087678 | -0.0258097 | -0.0440578 | 0.1334619 | 0.0467554 | 0.0026944 | -0.0086832 | 0.0393676 | 0.0513975 | 0.0298580 | 0.0123489 | 0.0509831 | 0.0093444 | 0.0519119 | 0.2303938 | 0.2506376 | 0.1742084 | 1.0000000 | 0.0056987 | 0.0878105 | 0.0949467 |
| Score_PSI_14 | Score_PSI_14 | 0.0140012 | -0.0172231 | 0.0399935 | 0.0169512 | 0.0723872 | 0.0676972 | 0.0163779 | -0.0166146 | 0.0086141 | 0.0498603 | 0.0454462 | 0.0734846 | 0.0647245 | 0.0464407 | 0.0492194 | 0.0596798 | 0.0652098 | 0.0527115 | 0.0228045 | 0.1207438 | 0.0453739 | -0.0093577 | 0.0522204 | 0.0056987 | 1.0000000 | 0.1176726 | -0.0181150 |
| Score_PSI_15 | Score_PSI_15 | -0.0158282 | 0.0304634 | 0.0144400 | 0.0430812 | 0.0625753 | 0.0374530 | 0.0419068 | -0.0268805 | 0.0326658 | 0.0433809 | 0.0297688 | 0.0340007 | 0.0342374 | 0.0029691 | 0.0625191 | 0.0999683 | 0.1018205 | 0.0910520 | 0.0127268 | 0.2197254 | 0.0830134 | 0.0464067 | 0.1358951 | 0.0878105 | 0.1176726 | 1.0000000 | -0.0467071 |
| Payment_PAYM_90_HIP_KNEE | Payment_PAYM_90_HIP_KNEE | 0.2975679 | -0.2108956 | 0.0053946 | -0.0627505 | -0.0720431 | -0.0241965 | -0.0815209 | -0.0048449 | -0.1256363 | 0.3410864 | 0.0591548 | -0.0406696 | -0.0350247 | -0.0062985 | -0.0272101 | 0.0086745 | -0.0766302 | 0.0456525 | -0.0041983 | -0.0237660 | 0.0497447 | 0.1441986 | 0.0655557 | 0.0949467 | -0.0181150 | -0.0467071 | 1.0000000 |
Imputation and Handling of Missing Values (AC)
# Remove all NA values in target variable "PredictedReadmissionRate_HIP_KNEE"
HipKneeTrain <- HipKneeTrain %>% filter(!is.na(PredictedReadmissionRate_HIP_KNEE))
# Remove all NA values in the "State", "StateCode", and "FacilityName" columns
HipKneeTrain <- HipKneeTrain %>% drop_na(State, StateCode, FacilityName)
# Print number of remaining variables and observations
dimensions <- dim(HipKneeTrain)
cat("Number of variables:", dimensions[2], "\n")## Number of variables: 31cat("Number of observations:", dimensions[1], "\n")## Number of observations: 1833We decided to remove the one facility that had an NA value, which also happened to be the same observation with a missing state value.
# Calculate missing values
missing_values_summary <- HipKneeTrain %>%
summarise(across(everything(), ~ sum(is.na(.)))) %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Missing_Count") %>%
mutate(Missing_Percentage = (Missing_Count / nrow(HipKneeTrain)) * 100)
# Print table
missing_values_summary %>%
kable(caption = "Table 7. Missing Values Summary") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | Missing_Count | Missing_Percentage |
|---|---|---|
| FacilityId | 0 | 0.0000000 |
| PredictedReadmissionRate_HIP_KNEE | 0 | 0.0000000 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 33 | 1.8003273 |
| EDV | 90 | 4.9099836 |
| HCP_COVID_19 | 16 | 0.8728860 |
| IMM_3 | 16 | 0.8728860 |
| OP_18b | 75 | 4.0916530 |
| OP_29 | 222 | 12.1112930 |
| SAFE_USE_OF_OPIOIDS | 69 | 3.7643208 |
| VTE_1 | 994 | 54.2280415 |
| Score_COMP_HIP_KNEE | 40 | 2.1822149 |
| Score_MORT_30_AMI | 405 | 22.0949264 |
| Score_MORT_30_COPD | 247 | 13.4751773 |
| Score_MORT_30_HF | 141 | 7.6923077 |
| Score_MORT_30_PN | 125 | 6.8194217 |
| Score_MORT_30_STK | 284 | 15.4937261 |
| Score_PSI_03 | 8 | 0.4364430 |
| Score_PSI_04 | 575 | 31.3693399 |
| Score_PSI_06 | 2 | 0.1091107 |
| Score_PSI_08 | 2 | 0.1091107 |
| Score_PSI_09 | 2 | 0.1091107 |
| Score_PSI_10 | 41 | 2.2367703 |
| Score_PSI_11 | 40 | 2.1822149 |
| Score_PSI_12 | 2 | 0.1091107 |
| Score_PSI_13 | 42 | 2.2913257 |
| Score_PSI_14 | 87 | 4.7463175 |
| Score_PSI_15 | 29 | 1.5821058 |
| FacilityName | 0 | 0.0000000 |
| State | 0 | 0.0000000 |
| Payment_PAYM_90_HIP_KNEE | 42 | 2.2913257 |
| StateCode | 0 | 0.0000000 |
Impute variables with low percentage missingness (<5%) by the median for numeric variables and mode for categorical variables
# Calculate median for columns with <5% missing values
numeric_vars_low_missing <- c("HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE", "EDV", "HCP_COVID_19", "IMM_3", "OP_18b", "SAFE_USE_OF_OPIOIDS", "Score_COMP_HIP_KNEE", "Score_PSI_03", "Score_PSI_06", "Score_PSI_08", "Score_PSI_09", "Score_PSI_10", "Score_PSI_11", "Score_PSI_12", "Score_PSI_13", "Score_PSI_14", "Score_PSI_15", "Payment_PAYM_90_HIP_KNEE")
for (var in numeric_vars_low_missing) {
HipKneeTrain[[var]][is.na(HipKneeTrain[[var]])] <- median(HipKneeTrain[[var]], na.rm = TRUE)
}Impute high percentage missingness variables (>5%) using KNN
# Select high missingness variables for KNN imputation
vars_for_knn <- c("VTE_1", "Score_MORT_30_AMI", "Score_MORT_30_COPD", "Score_MORT_30_HF", "Score_MORT_30_PN", "Score_MORT_30_STK", "Score_PSI_04", "OP_29")
# Perform KNN imputation
HipKneeTrain_knn <- kNN(HipKneeTrain, variable = vars_for_knn, k = 5)
# Remove columns created by the KNN function
HipKneeTrain_knn <- HipKneeTrain_knn %>% select(-ends_with("_imp"))
# Update HipKneeTrain with imputed values
HipKneeTrain[vars_for_knn] <- HipKneeTrain_knn[vars_for_knn]# Calculate missing values
missing_values_summary <- HipKneeTrain %>%
summarise(across(everything(), ~ sum(is.na(.)))) %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Missing_Count") %>%
mutate(Missing_Percentage = (Missing_Count / nrow(HipKneeTrain)) * 100)
# Print table
missing_values_summary %>%
kable(caption = "Table 7. Missing Values Summary") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | Missing_Count | Missing_Percentage |
|---|---|---|
| FacilityId | 0 | 0 |
| PredictedReadmissionRate_HIP_KNEE | 0 | 0 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 0 | 0 |
| EDV | 0 | 0 |
| HCP_COVID_19 | 0 | 0 |
| IMM_3 | 0 | 0 |
| OP_18b | 0 | 0 |
| OP_29 | 0 | 0 |
| SAFE_USE_OF_OPIOIDS | 0 | 0 |
| VTE_1 | 0 | 0 |
| Score_COMP_HIP_KNEE | 0 | 0 |
| Score_MORT_30_AMI | 0 | 0 |
| Score_MORT_30_COPD | 0 | 0 |
| Score_MORT_30_HF | 0 | 0 |
| Score_MORT_30_PN | 0 | 0 |
| Score_MORT_30_STK | 0 | 0 |
| Score_PSI_03 | 0 | 0 |
| Score_PSI_04 | 0 | 0 |
| Score_PSI_06 | 0 | 0 |
| Score_PSI_08 | 0 | 0 |
| Score_PSI_09 | 0 | 0 |
| Score_PSI_10 | 0 | 0 |
| Score_PSI_11 | 0 | 0 |
| Score_PSI_12 | 0 | 0 |
| Score_PSI_13 | 0 | 0 |
| Score_PSI_14 | 0 | 0 |
| Score_PSI_15 | 0 | 0 |
| FacilityName | 0 | 0 |
| State | 0 | 0 |
| Payment_PAYM_90_HIP_KNEE | 0 | 0 |
| StateCode | 0 | 0 |
Feature Engineer Mortality Data (AC)
# Average death rates amongst mortality variables and create new column "Score_Ovr_MORT"
HipKneeTrain$Score_Ovr_MORT <- rowMeans(HipKneeTrain[, c("Score_MORT_30_AMI",
"Score_MORT_30_COPD",
"Score_MORT_30_HF",
"Score_MORT_30_PN",
"Score_MORT_30_STK")],
na.rm = TRUE)
# Remove old mortality columns
HipKneeTrain <- HipKneeTrain[, !(names(HipKneeTrain) %in% c("Score_MORT_30_AMI",
"Score_MORT_30_COPD",
"Score_MORT_30_HF",
"Score_MORT_30_PN",
"Score_MORT_30_STK"))]Reassess heatmap with engineered mortality data
# Compute correlation matrix
cor_matrix <- cor(HipKneeTrain %>% select_if(is.numeric), use = "pairwise.complete.obs")
# Melt the correlation matrix into a long format
cor_melted <- melt(cor_matrix)
# Plot heatmap
ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), name = "Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Figure 5. Correlation Heatmap of Numeric Variables")
> Descriptive Statistics (Adeline)